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Development of a California Geospatial Intermodal Freight

Transport Model with Cargo Flow Analysis

Contract No.: 07-314

Principal Investigator:

James J. Corbett, PhD.

University of Delaware

Co-Principal Investigators:

J. Scott Hawker, PhD.

Rochester Institute of Technology

James J. Winebrake, PhD.

Rochester Institute of Technology

12/6/2010

Prepared for the California Air Resources Board and the California Environmental Protection Agency

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DISCLAIMER

The statements and conclusions in this Report are those of the contractor and not necessarily those of the California Air Resources Board. The mention of commercial products, their source, or their use in connection with material reported herein is not to be construed as actual or implied endorsement of such products.

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ACKNOWLEDGMENTS

The following individuals contributed to this research report:

Richard Billings, Eastern Research Group James J. Corbett, University of Delaware Arindam Ghosh, Rochester Institute of Technology J. Scott Hawker, Rochester Institute of Technology Karl F. Korfmacher, Rochester Institute of Technology Earl E. Lee, University of Delaware Jordan A. Silberman, GIS Consulting James J. Winebrake, Rochester Institute of Technology

This Report was submitted in fulfillment of contract 07-314, Development of a California Geospatial Intermodal Freight Transport Model with Cargo Flow Analysis, by The University of Delaware and Rochester Institute of Technology under the partial sponsorship of the California Air Resources Board. Work was completed as of November 15, 2010.

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Table of Contents List of Figures .............................................................................................................................................. vi List of Tables .............................................................................................................................................. vii Abstract ...................................................................................................................................................... viii Executive Summary ..................................................................................................................................... ix 1 Introduction ........................................................................................................................................... 1

1.1 Project Purpose ............................................................................................................................. 1 1.2 Background ................................................................................................................................... 2

2 Methodology ......................................................................................................................................... 2 2.1 Overview ....................................................................................................................................... 2 2.2 The GIFT Model ........................................................................................................................... 4 2.3 Structure of the GIFT Model ........................................................................................................ 6

2.3.1 Transportation “Costs” .......................................................................................................... 7 2.3.2 Transportation Network Geospatial Data ............................................................................ 10 2.3.3 Intermodal Facilities Geospatial Data ................................................................................. 11 2.3.4 Operational Characteristics ................................................................................................. 12 2.3.5 Modeling Rail Dwell Times ................................................................................................ 14 2.3.6 Origin- Destination Freight Flow Data ............................................................................... 15

2.4 Evaluation of the freight flow origination and destination and volume model ........................... 16 3 Case Study .......................................................................................................................................... 18

3.1 Data Sources ............................................................................................................................... 19 3.1.1 Cambridge Systematics Origin-Destination Database ........................................................ 19 3.1.2 Port Container Data ............................................................................................................. 19

3.2 Assumptions for the Model ......................................................................................................... 20 3.2.1 Emission rates ..................................................................................................................... 21 3.2.2 Assumptions for Intermodal Transfers ................................................................................ 22 3.2.3 Travel Time ......................................................................................................................... 23

4 Case Study Results .............................................................................................................................. 24 4.1 Least-time Route Emissions ........................................................................................................ 24 4.2 Least-CO2 Route Emissions ........................................................................................................ 27 4.3 Comparison of Emissions across Scenarios ................................................................................ 30

5 Case Study Discussion ........................................................................................................................ 32 6 Summary and Conclusions.................................................................................................................. 33 7 Recommendations ............................................................................................................................... 34 8 References ........................................................................................................................................... 35 Glossary ...................................................................................................................................................... 38 APPENDIX A: Data Summary Sheets for Mobile Sources ........................................................................ 40 APPENDIX B: Using the Model in Case Study ....................................................................................... 112

How to Run a Multiple OD-Pair Route Analysis in ArcGIS Network Analyst .................................... 112 Importing Multiple OD Sets into Network Analyst (METHOD 1) ...................................................... 112 Importing Multiple OD Sets into Network Analyst (METHOD 2) ..................................................... 113 Adding in CFS Freight Totals, Weights, and Destination Estimated TEUs ......................................... 115 Add Network Analyst Traversal Result To ArcMap ............................................................................ 116 Creating Unique IDs for the Edge Features .......................................................................................... 118 Calculating the Number of Times a Network Segment is Used for a Given Port Analysis .................. 118

APPENDIX C: Recommended Emissions, Cost and Energy Data Sources ............................................. 121 APPENDIX D: Validating Intermodal Facilities ...................................................................................... 123

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APPENDIX E: Calculating Emissions from First Principles ................................................................... 127 APPENDIX F: Creation of Origin-Destination Volume Flow Model ...................................................... 130

Approach 1 – Distributing Freight at the CSA/MSA Level ................................................................... 136 Approach 2 – Distributing Freight at the County Level ....................................................................... 136 Approach 3 – Distributing Freight at the Sub-County Level ................................................................ 139

APPENDIX G: Cambridge Systematics Inc FAF2 Disaggregation Methodology ................................... 147

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List of Figures

Figure 1. Example Freight Network from “A” to “B” .................................................................................. 3 Figure 2. The GIFT Intermodal network ...................................................................................................... 4 Figure 3. Connecting Road, Rail and Waterway networks at Intermodal Facilities ..................................... 5 Figure 4. Intermodal Freight Transport Model Example .............................................................................. 6 Figure 5. Structure and Use of the GIFT Model ........................................................................................... 7 Figure 6. "Cost" attributes associated with transportation network segments .............................................. 8 Figure 7. Tool to define and manage case study analysis values. ................................................................ 9 Figure 8. Computing Emissions and Energy from First Principles ............................................................ 10 Figure 9. Verifying Facility location using Google Earth ........................................................................... 12 Figure 10. Defining Operational Characteristics ........................................................................................ 13 Figure 11. Modeling Dwell for the Port of LA-Long Beach ...................................................................... 15 Figure 12. Top 25 Container Ports U.S. 2008 (Source: BTS, US DoT) ..................................................... 18 Figure 13. Freight Flow Model .................................................................................................................. 20 Figure 14. Emissions intensity for different modes .................................................................................... 22 Figure 15. Representing Intermodal Facilities ............................................................................................ 22 Figure 16. Container Traffic from Ports (Least-time Scenario) .................................................................. 25 Figure 17. Container Traffic to Ports (Least-time Scenario) ...................................................................... 26 Figure 18. Air Basin Emissions (Least-time scenario) .............................................................................. 27 Figure 19. Container Traffic from Ports (Least-CO2 Scenario) .................................................................. 28 Figure 20. Container Traffic to Ports (Least-CO2 Scenario) ....................................................................... 29 Figure 21. Air Basin Emissions (Least-CO2 scenario) ................................................................................ 30 Figure 22. Emission Variations by Air Basin ............................................................................................. 31 Figure 23. LA-Long Beach total route counts per network segment (edge) ............................................ 119 Figure 24. LA-Long Beach total TEUs per network segment (edge) ...................................................... 120 Figure 25. Rail carrier facility spreadsheet ............................................................................................... 123 Figure 26. Facilities spreadsheet as an imported shapefile ...................................................................... 124 Figure 27. Facilities attributes table ......................................................................................................... 125 Figure 28. Panning location ..................................................................................................................... 125 Figure 29. CFS data for Los Angeles - Long Beach Area (Source: Commodity Flow Survey 2007) ...... 131 Figure 30. California CFS Regions ........................................................................................................... 132 Figure 31. Top 25 Container Ports 2008 (Source: BTS, U.S. DoT) ......................................................... 133 Figure 32. CFS Freight Distribution for LA/LB ...................................................................................... 135 Figure 33. CA Counties and associated CFS regions (Source: California Dept of Finance, State of California) ................................................................................................................................................. 138 Figure 34. Census Geographic Areas (Source: US Census GARM Ch2) ................................................. 139 Figure 35. LA County Incorporated Places (Source: LA County Chamber of Commerce) ..................... 140 Figure 36. Distributing freight flow .......................................................................................................... 142 Figure 37. Process Workflow for freight distribution ............................................................................... 144 Figure 38. CFS Destinations in Arizona (Source: Google Maps™) ......................................................... 145 Figure 39. O/D pair Data Set for Oakland port Area ................................................................................ 145 Figure 40. Building the O/D pair framework ............................................................................................ 146 Figure 41. CFS destinations for the West Coast Ports .............................................................................. 146

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List of Tables

Table 1. Databases evaluated for Cal-GIFT Project ................................................................................... 11 Table 2. Approaches to disaggregate flow data ......................................................................................... 17 Table 3. Port Container Statistics ................................................................................................................ 20 Table 4. Intermodal Transfer Emissions ..................................................................................................... 23 Table 5. Least-time Route Emissions ......................................................................................................... 26 Table 6. Least-CO2 Route Emissions .......................................................................................................... 29 Table 7. Emissions Comparison for Entire Routes of All O-D Pairs .......................................................... 31 Table 8. Emissions by Air Basin ................................................................................................................. 32 Table 9. Comparing port containers and freight tonnage ............................................................................ 33 Table 10. Recommended emissions, cost, and energy data sources ......................................................... 121 Table 11. West Coast Port Statistics (Source: USACE) .......................................................................... 134

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Abstract

This project further develops the Geospatial Intermodal Freight Transportation (GIFT) model, configures the model with California-specific data, and uses the configured model in a case study of the possible benefit of shifting freight transportation from trucks to rail. The result is a model that describes the energy and environmental impacts of goods movement through California’s marine, highway, and rail systems. The GIFT research team has employed a Geographic Information System (GIS)-based model that integrates three transportation network models (road, rail, water), joined by intermodal transfer facilities (ports, railyards, truck terminals) in a single GIS “intermodal network” modified to capture energy and environmental attributes. A Case Study was performed to explore the difference in emissions under Least-travel-time versus least-CO2 routing of goods movements, identifying how emissions savings can be achieved through modal shifts from road to rail. The Case Study estimates CO2 emissions to be approximately 2.89 million metric tons (MMT) of CO2 attributable to the container traffic of the three major West Coast ports (LA-Long Beach, Oakland and Seattle) using a least-time scenario (which comprises mostly trucks). Our estimation of a total reduction of approximately 1.7 MMT of CO2 occurs through a nationwide modal shift of West Coast port-generated goods movement; within California state air basins, this reduction is near 0.5 MMT CO2. Overall, this research demonstrates how the GIFT model, configured with California-specific data, can be used to improve understanding and decision-making associated with freight transport at regional scales.

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ExecutiveSummary

Background

California represents a major international gateway for goods movement and is a domestic partner of other states providing goods movement for North America. California has also become a leader in improving transportation environmental and energy performance. U.S. reliance on the freight transportation system has been growing considerably for some time ( Bureau of Transportation Statistics, 2005; Greening, Ting, & Davis, 1999; Schipper, Scholl, & Price, 1997b; Vanek & Morlok, 2000). These trends are likely to continue in the coming decades due to increasing international and domestic trade (U.S. Energy Information Administration, 2007). Many researchers expect that along with this increase in overall freight transport there will be an increase in intermodal freight transport where goods are moved along a combination of highways, railways, and waterways (Arnold, Peeters, & Thomas, 2004; Ballis & Golias, 2002, 2004; T. Golob & Regan, 2001; T. F. Golob & Regan, 2000; Shinghal & Fowkes, 2002). With this increasing freight transport activity, it is expected that congestions, emissions, and energy use will increase at a similar pace (Komor, 1995; Koopman, 1997; Schipper, Scholl, & Price, 1997a). Policymakers and planners must develop operational and infrastructure improvement strategies to increase the efficiency of freight movement to reduce demand for transportation fuels and mitigate environmental impacts (Nijkamp, Reggiani, & Bolis, 1997). The Geospatial Intermodal Freight Transportation (GIFT) model includes highway, railway, and waterway transportation networks of the U.S. and Canada, plus the international ocean shipping network. GIFT integrates these three transportation modes at intermodal transfer facilities, including ports, railyards, and truck terminals; freight can move from one transportation mode to another through these facilities. Along with the intermodal transportation network model, GIFT provides models of trucks, trains, and marine vessels, capturing their emissions, energy use, operating cost, and operational characteristics such as speed and freight capacity. By combining these in a Geographic Information System (GIS) with built-in route optimization computations, GIFT can find transportation routes that are the shortest distance, least emissions, least time, least operating cost, and least energy. Adding in models of the freight volume and shipping origins and destinations, GIFT helps agencies and researchers understand the environmental, economic, and energy impacts of freight transportation and tradeoffs of alternate improvement decisions. Methods

In this research, we improved the GIFT model and we configured the model with California-specific data on freight volume and origins and destinations for port-generated traffic (freight entering or leaving the major west-coast ports, including Los Angeles, Long Beach, Oakland, and Seattle, Washington). We compiled international, national, and California-specific data from a number of public and proprietary sources. These include data on shipping origins and destinations; freight volumes; truck, train, and ship performance and costs; and intermodal transfer facility performance and costs. We evaluated advantages and shortcomings of each data source. We found that publicly available data was sufficient quality to be included in California-specific GIFT modeling.

We then modified the GIFT model to meet California port-generated study objectives, and evaluated model performance through a case study. We demonstrated that GIFT can be configured with a variety of data sets, each selected to address the specific environmental, economic and energy characteristics of goods movement of interest in a specific case study. We documented how to configure GIFT with specific data and how to use the resulting model to perform case studies.

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We then performed a case study to illustrate use of the model for estimating international and domestic goods movement in all modes against available commodity flow data in selected regions of California (i.e., regions near ports). The case study compared the difference in emissions under least-travel-time versus least-CO2 routing of goods movements through three major California ports (Los Angeles, Long Beach, and Oakland) and through the Port of Seattle, Washington. The case study identified essential trade-offs and provided recommendations on steps to improve, validate, or expand case study results.

Results

Using the GIFT model with California-specific data on the transportation network, intermodal facilities, vehicle performance (energy, emissions, operating cost), and freight flow (origins, destinations, and volumes), we characterized the least-time and least-CO2 emissions freight flows. We found least-time routes were dominated by truck traffic along parts of interstates I-5, I-10, I-15, I-40, and I-90. The model estimated a total of approximately 2.9 million metric tons (MMT) of CO2 emissions occur over the course of the year due to freight moving in and out of these three ports on the West coast (assuming that all freight moves by truck). Of these, the majority of emissions (~79% of total) are due to traffic moving in and out of the port of Los Angeles-Long Beach.

In the least-CO2 scenario, most freight was routed through the rail network because of low emissions involved with moving freight by train. Our estimation of a total reduction of approximately 1.7 MMT of CO2 occurs through a nationwide modal shift of West Coast port-generated goods movement; within California state air basins, this reduction is near 0.5 MMT CO2.

Conclusions

The Case Study provides two primary insights. First, the Case Study quantifies port-related intermodal goods movement through the state of California and beyond. Second, the idealized use of least-CO2 routing constraints illustrates how emissions savings can be achieved through modal shifts. In terms of savings in emissions, it is estimated that a total of ~60% reduction in CO2 emissions is achievable by a modal switch from road to rail. Both of these insights have relevance for consideration of system-wide improvements that may achieve energy savings, CO2 reductions, and associated benefits for air quality.

The GIFT model provides the necessary flexibility and configurability to incorporate case-specific and region-specific data from numerous sources. Application of the GIFT model in other projects has been of significant value to regional and national goods movement evaluation and planning. Configured with California-specific data, the GIFT model results may be of significant value to the Air Resources Board in evaluating tradeoffs among numerous environmental, energy, and economic attributes of goods movement in the State of California. In the future, GIFT can continue to be an important analytical and planning tool for California decision makers. We identified further opportunities for similar trade-off case studies and for model improvements.

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1 Introduction

1.1 Project Purpose The project purpose is to further develop the Geospatial Intermodal Freight Transportation (GIFT) model and provide it with California-specific data and inputs, resulting in an intermodal freight transport model that describes the energy and environmental impacts of goods movement through California’s marine, highway, and rail systems. Employing a Geographic Information System-based (GIS) model that integrates three model networks (road, rail, water) in a single GIS “intermodal network” modified to capture energy and environmental attributes, the project will contribute to improved decision-making associated with freight transport at regional scales. Specifically, the model will allow evaluation of: (1) the energy and environmental impacts associated with California freight movement; (2) decisions related to various highway and intermodal facility infrastructure development and resiliency; and, (3) decisions aimed at improving freight movement efficiency in California (see Task 1 technical memorandum).

The project included five main tasks, each with a technical memorandum as a deliverable. These tasks were as follows:

Task 1: Refined research plan. This task presented a research plan in consultation with ARB staff. The submitted research plan contained a work plan, project schedule, and a review of the relevant research work, data sources, and literature. This plan formed the basis for focus on energy and environmental attributes of the goods movement related to western ports (Los Angeles/Long Beach, Oakland, and Seattle).

Task 2: Data compilation plan. This task obtained and reviewed data from sources identified in the RFP and during the development of the Task 1 Refined Research Plan. We evaluated the advantages and shortcomings of each data source and compiled the data for subsequent tasks. The technical memorandum from this task included discussion of assumptions made and surrogate data developed to fulfill required data elements. The memorandum also summarized the strengths and limitations of the compiled data. This data compilation identified data with sufficient quality to be included in California-specific modeling to meet the goals of this project.

Task 3: Model selection and modification. This task focused on two activities: (i) selection of an appropriate model; and, (ii) modification of the model to meet project objectives. The task concluded with a memorandum that described the selection, formulation, and modification of the model. The GIFT model was selected as appropriate to use the data compiled in executing the research plan.

Task 4. Model evaluation. This task evaluated the intermodal freight transport model developed in Task 3 using California-specific data compiled in Task 2. The task concluded with a technical memorandum written that described the evaluation of the model. This evaluation determined that GIFT can successfully use several data sets to evaluate the energy and environmental characteristics of goods movement related to port activity, and determined that origin-destination information recently provided to the ARB through independent contract could be used in the case study. Case study specifications were finalized in the model evaluation task.

Task 5: Case study. This task involved development of a case study to illustrate the model performance for estimating international and domestic goods movement in all modes against available commodity flow data in a selected region of California. The case study compared environmental tradeoffs associated with alternate routing of goods movement through major state ports. The task concluded with a technical memorandum that described model performance for the case study, and provided recommendations on steps to improve, validate, or expand case study results.

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This final report represents a summary of the project and is the final deliverable.

1.2 Background California represents a major international gateway and domestic partner of goods movement for other states, and has become a leader in improving environmental and energy performance of transportation. U.S. reliance on the freight transportation system has been growing considerably for some time. (Bureau of Transportation Statistics, 2005; Greening, Ting, & Davis, 1999; Schipper, Scholl, & Price, 1997b; Vanek & Morlok, 2000) These trends are likely to continue in the coming decades due to increasing international and domestic trade. Many researchers expect that along with this increase in overall freight transport there will be an increase in intermodal freight transport (Arnold, Peeters, & Thomas, 2004; Ballis & Golias, 2002, 2004; T. Golob & Regan, 2001; T. F. Golob & Regan, 2000; Shinghal & Fowkes, 2002).

With increasing freight transport activity, it is expected that congestion, emissions, and energy use will increase at a similar pace (Komor, 1995; Koopman, 1997; Schipper, Scholl, & Price, 1997a). For example, currently, freight transport emits about 470 million metric tonnes of CO2 (MMTCO2) per year, or about 8.3% of fossil fuel CO2 combustion emissions, and about 7.8% of total CO2 emissions (U.S. Energy Information Administration, 2007; U.S. Environmental Protection Agency, 2006). Policymakers and planners must develop operational and infrastructure improvement strategies to increase the efficiency of freight movement to reduce demand for transportation fuels and mitigate environmental impacts (Nijkamp, Reggiani, & Bolis, 1997).

Operationally, intermodal freight transport sustainability is understudied both in terms of theory and application, and the environmental impacts of such transport are only beginning to be evaluated systematically (Bontekoning, Macharis, & Trip, 2004; Macharis & Bontekoning, 2004). Researchers need to develop new methodologies, data management techniques, and computing infrastructure, and to integrate these into analytical tools that can be used to improve planning and decision making. The model developed by our interdisciplinary team of researchers and transportation professionals can assist in improving the environmental performance of goods movement. The model uses currently available commodity flow, vehicle activity, emissions, and other data to describe ocean-going vessel, truck, and rail emissions associated with goods movement in and through the state. Moreover, it recognizes that freight data will improve over time and can flexibly accept best data for modes, ports, and transfer facilities. This model will provide capacity to evaluate alternative strategies to improve performance and meet targets for energy conservation, air quality, and CO2 reduction. This work is consistent with California research objectives described in the 2007-2008 Air Pollution Research Plan (Mora & Barnett, 2007).

2 Methodology

2.1 Overview This project closely relates to research our team has been conducting at the national and regional level. In particular, several projects for the U.S. Department of Transportation and the Great Lakes Maritime Research Institute co-funded an initiative to develop an intermodal freight network optimization model that is now named GIFT. The GIFT model was the first geospatial model to explicitly include energy and environmental objectives (e.g., least carbon emissions, least Particulate Matter (PM) emissions, least NOx emissions, etc.) in its optimization routines (Falzarano et al., 2007; Hawker et al., 2007; Winebrake, Corbett, & Meyer, 2007). GIFT demonstrated an approach that allowed decision makers to quantify the energy and environmental impacts associated with freight transport, and importantly, to compare alternative modes and routes and their impact on a range of energy and environmental attributes. These comparisons allow for tradeoff analysis (e.g., least cost v. least carbon cargo flows).

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Our current understanding of some of these models’ benefits and limitations has proven to be valuable, particularly with regard to our development of GIS-network models for energy and emissions; we believe this is an advantage to ARB. A general approach of GIS-network models can be illustrated through a simple example. Figure 1shows a network of alternative pathways to move freight from point A to point B. Freight can move along pathways through each node (shown by the circles). Certain network segments (represented by lines connecting the nodes) may be accessible only by truck, or ship, or rail. Some points may be accessible by multiple modes. Nodes and segments can be associated with metropolitan traffic characteristics, descriptive of congestion delays, engine load, and emissions patterns that may differ from open freeway, long-haul rail, and/or interport segments.

Figure 1. Example Freight Network from “A” to “B”

When developed to be a descriptive model of multimodal freight activity, we solve the network according to least cost transport of freight from A to B, a traditional context for the application of optimization routines. (Note that we use the term “cost” in a generalized optimization modeling context to reflect the objective that we wish to minimize for a given network analysis problem). To analyze routes, each route from node i to node j must include “attribute data” that helps characterize attributes along that route. That dataset could include information about mode accessibility, economic costs, average speed, distance, emissions, among others.

Recognizing the value of other GIS based freight analysis tools, such as the Freight Analysis Framework (FAF) and GeoMiler (Lewis & Ammah-Tagoe, 2007), we worked to explicitly integrate energy and environmental attributes into freight network analyses in GIS. This was the first time that a team fully integrated energy and environmental emissions attributes, such as carbon emissions, into the ArcGIS network analysis environment in order to conduct environmental impacts studies associated with freight movement. We applied these approaches regionally through a funded project to study the environmental characteristics of freight transportation in the Great Lakes Region.

By adding energy and environmental attribute information to segments of the national highway, rail, and waterway network, we can report environmental performance measures associated with current freight flows (Figure 3). In this way, such a model could directly address project requirements for this research. When run with existing freight route data, such a model could output the energy and environmental impacts associated with cargo flows along the network. In addition, the model could also be programmed to evaluate alternative cargo flow patterns that minimize energy consumption and emissions of CO2, PM10, NOx, SOx, and volatile organic compounds (VOCs) and compare these network solutions with

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a. Originations and destinations of cargo entering or leaving California ports b. Volumes of cargos along the various origination and destination paths

Figure 5. Structure and Use of the GIFT Model

2.3.1 Transportation“Costs”A primary purpose of the GIFT model is to use operational costs, time-of-delivery, energy use, and emissions from freight transport to evaluate tradeoffs among these criteria in an intermodal routing context. To accommodate a wide variety of operational scenarios, we developed multiple ways to define, manage, and use “costs.” The main concept was to associate these costs with traversing each segment of the transportation network, and to provide multiple ways to make the specific route cost depend on the vehicle type, fuel choice, operational and governmental policy in force, and other scenario attributes. Figure 6 illustrates various costs of traversing segments in the network. Different ways to model these

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• Select cost attributes to minimize

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Geospatial Intermodal Freight Transportation (GIFT) Analysis

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Scenario Analysis Results

Find Least “Cost” Routes

8

costs serve different modeling needs, and it is important to understand which options are incorporated into a given model and how they are used at run-time to compute transportation costs.

In ArcGIS, these costs are defined as network attributes in the network geodatabase. Some attributes are predefined in the network datasets and have fixed values (such as the distance attribute), some are predefined attributes whose values can change (such as using posted highway speed limits or observed truck speeds for different speed values along different highway segments), and some are attributes added specifically to support GIFT (emissions, speed). The impacts of transportation through intermodal facilities are similarly captured as attribute values on “spokes” created to model intermodal transfers, where attribute values model the impact of freight handling equipment, facility energy use, delays loading and unloading ships, etc.

Figure 6. "Cost" attributes associated with transportation network segments

The values for network attributes can be accessed during network analysis run-time (that is, during least-cost optimization or during computations of route data for determined routes) in multiple ways, depending on the data used and their source. Some data are stored statically in the network geodatabase (such as segment distance or posted speed limit), some are computed using Visual Basic scripts embedded into and stored with the database (using ArcCatalog and ArcGIS Network Analyst utilities), and some values are computed using external computations (“custom evaluators” in Network Analyst terminology) that we implemented as C# program components registered in the ArcGIS run-time framework. The embedded computation evaluators can use any data defined as attributes in the network model, whereas the custom evaluators can also access external data and computations. Assigning attribute values or associating evaluators with attributes is performed using ArcCatalog while building the transportation network geodatabase.

Most of the attribute values are computed using custom evaluators that access data that the analyst can modify to reflect differing operational scenarios. The model incorporates a user interaction and data management tool, illustrated in Figure 7, to define and manage cost factors used by the external evaluators. For roadway, waterway, and railway attributes, the custom evaluator simply multiplies the segment distance by the configured cost factor. Values obtained from other sources (e.g., California-specific values and settings) can be entered, saved and reused across multiple analyses.

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11

Table 1. Databases evaluated for Cal-GIFT Project

DATABASE ROAD RAIL WATER FACILITIES National Transporation Atlas Database (NTAD) Yes Yes Yes Yes US Army Corps Engineers (USACE) No No Yes Yes Streetmap USA (2008 TeleAtlas) Yes Yes No No STEEM (University of Delaware) No No Yes Yes ALK (ALK Technologies, www.ALK.com) Yes No No No GeoGratis/National Resource Canada Yes Yes Yes No GeoBase Canada (high detail) Yes No Yes No Land Information Ontario (Canada) Yes No Yes No Loadmatch Intermodal (www.loadmatch.com ) No No No Yes The Drayage Directory (www.drayage.com ) No No No Yes Railroad Performance Measures http://www.railroadpm.org/home/rpm.aspx

No Yes No Yes

The waterway network utilized the STEEM database from the University of Delaware, which is an international shipping database that describes ocean shipping lanes. Close to shore and inland, however, the NTAD and USACE data for waterways are more precise. The GIFT team combined the STEEM, NTAD, and USACE waterways, using STEEM outside of a 20 km buffer from the US coastline. NTAD and USACE data are used near shore and for river networks. The ports database of the USACE and STEEM were used to help determine major intermodal port facilities.

Apart from the open access databases, the proprietary databases that were evaluated included the Streetmap USA (2008) database and the ALK database. The Streetmap USA database includes posted speed limits as an attribute for the road segments, typically assigned by road class. Streetmap USA classifies roads based on a use/volume hierarchy that is independent of the posted speed limit. The proprietary ALK database provides average empirical speed data based on GPS observation records, as opposed to posted speed limits. Additionally, the ALK data contain attribute information on speed by traffic flow direction. Both Streetmap USA and NTAD provide comparable rail networks (there are minor geographic differences between the two), but neither database includes rail speeds by segment. While these proprietary databases contain additional information, they were considered to be too fine-grained for the project purpose, possibly resulting in computing performance problems, and the added detail was deemed not necessary for regional flow studies.

The GIFT transportation network data for highways, railroads, and waterways is thus based largely on the NTAD 2008 data (www.bts.gov/publications/national_transportation_atlas_database/2008/), which is a compilation of data including the National Highway Planning Network, the U.S. Army Corps of Engineers Navigable Waterway Network, and the National Railway Network). These data are at an appropriate level of granularity for regional and national flow studies, they are geo-referenced with the transfer facility data we use, and they have been validated in other GIFT projects.

2.3.3 IntermodalFacilitiesGeospatialDataTransfer facility locations and supported mode type data were derived originally from the “Intermodal Terminal Facilities” NTAD 2008 data set. Focusing on California facilities, this data set was validated by visual inspection of the reported location using Google Earth. Google Earth’s bird’s eye view of geographical features allowed visual analysis of each point location to see if it either falls on or is close to something that looks like a facility (Figure 9). If this was found to be the case it was recorded as “verified”. The “TYPE” and “MODE_TYPE” fields in the facilities shapefile metadata indicate the primary transportation mode designated for the facility, and supported modes of traffic respectively. These data acted as clues as to what should be seen in Google Earth for a given facility. For instance, if a facility is a major rail depot that also handles truck traffic, then railroads, railcars, and trucks should be

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12

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14

2.3.5 ModelingRailDwellTimesIn a separate project, the GIFT team has begun to characterize potentially significant time penalties associated with dwell times at major intermodal terminals, such as rail terminals, port terminals, and truck terminals – a subset of intermodal connections where in-route freight storage may occur before transfer. Specifically, a U.S. DOT project identified initial dwell conditions generic to rail terminals, although these are not California-specific. According to the Railroad Performance Measures website, maintained by six major US rail freight carriers, “Terminal Dwell is the average time a car resides at the specified terminal location expressed in hours. The measurement begins with a customer release, received interchange, or train arrival event and ends with a customer placement (actual or constructive), delivered or offered in interchange, or train departure event. Cars that move through a terminal on a run-through train are excluded, as are stored, bad ordered, and maintenance of way cars” (http://www.railroadpm.org/Definitions.aspx).

Major terminals from each of the major rail freight carriers can be identified from the carriers’ own website maps and positioned using Google Earth coordinates. Dwell times may be assigned to each of these points, e.g., determined from the Railroad Performance Measures website.

Figure 11 illustrates potential dwell points surrounding the Ports of Long Beach and Los Angeles (LA-LB) using overlapping five-mile buffers. In most cases, the dwell time assigned to a point represents half of the total reported dwell time, so that a route accumulates the total dwell penalty after entering and leaving the dwell buffer area. In this LA-LB example, however, total dwell times would be assigned to the dwell points because the rail lines emanate from or terminate at the port. There are no rail lines simply passing through the buffer, as would be the case for a rail line within the interior of the US. To make this approach California-specific, ports should be examined for this characteristic and dwell times adjusted as needed (either being assigned the total dwell penalty if the route only passes through a single dwell point or a half value if the route passes through two points). The case study settings have generic defaults from the U.S. DOT project and therefore time accumulations would be considered prototype and are not reported as results for this case study. Given that rail segment speeds are much lower than road segment speeds on highways, the inclusion of default dwell buffers helps avoid rail in the least-time scenario but does not determine the route differences between least-time and least-CO2 case study runs.

Figure 11

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15

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16

The O-D pair data can be supplemented with the Army Corps of Engineers Entrance and Clearance data based on a vessel's International Maritime Organization (IMO) identification number to quantify the volume of container traffic entering and leaving the port. The Entrance and Clearance data can be linked to vessel-specific data compiled by classification societies such as Lloyd's registry of ships to provide details concerning operational characteristics of these vessels. Eastern Research Group (ERG) linked the two datasets together for other projects and typically can match 90 to 95 percent of vessels to their vessel characteristics. The Entrance and Clearance data also documents the previous and next port of call, which will provide reasonably accurate mapping of international cargo traffic patterns. These data are used to map out individual vessel movements, and this information can be applied to GIS tools to quantify distance between ports. This distance value can be divided by the vessel's speed as noted in the Registry of Ships to calculate hours of operation between ports. These transit times will have to be adjusted to account for operations in reduced speed zones and congestion while approaching and operating within ports.

According to our current data sources, most freight to/from California ports is not originated or destined for the port vicinity, rather, it moves to/from locations throughout California and the U.S. First drop or drayage data in the port region to a transfer facility for reconfiguration or mode change provides one set of O/D-volume data. The Freight Analysis Framework (FAF2) and the Commodity Flow Survey (CFS) data from the U.S. Department of Transportation Bureau of Transportation Statistics (http://www.bts.gov/publications/commodity_flow_survey) provide information on freight flows beyond the port region.

FAF2 and CFS provide data from the LA-LB and other port regions to and from final and original destinations. These locations are both those within the LA-LB Core Based Statistical Area (CBSA) and anywhere in the remaining US either by state, metropolitan area, or other geographic reference. For example, specific metropolitan area data are provided for the San Jose–San Francisco–Oakland, California CBSA, San Diego–Carlsbad–San Marcos, California Metropolitan Statistical Area (MSA), Sacramento––Arden–Arcade––Truckee, CA–NV CBSA (California Part), and Los Angeles–Long Beach–Riverside, California CBSA, with an aggregated entry for “remainder of California.” For locations designated as “remainder of state,” a location was selected as the centroid of the region (state, county), placed at an NTAD transfer facility nearest to that centroid location or at a probable transfer location based on a visual inspection of the road and rail data and Google Earth imagery.

For near-port traffic, ARB has provided a sample of survey data on drayage trips, that is, trips from the ports to the first stop in the LA-LB area. The ARB survey data provided address information for destinations. Many of the addresses provided were to freight company administrative offices and not the warehouse locations. This sample data set has been reviewed and best GIS positional match to actual warehouses was completed as a trial run for the batch mode of the model. As more complete and accurate regional O/D-volume data become available, a similar approach to validating that data and incorporating them into the California GIFT O/D-volume data sets can be performed.

For a given case study, analysts can select the O/D-volume data derived from the FAF2, CFS, and regional data provided in the California configuration of the GIFT model, or the analysts can incorporate other data that may be provided. Combining these data, the research team can model the trip from the port to the first stop and from these drayage points to final destinations.

2.4 Evaluationofthefreightfloworiginationanddestinationandvolumemodel

Freight flow data are derived from U.S. Commodity Flow Statistics (CFS) and Freight Analysis Framework, Version 2 (FAF2) data. The geographic location of originations and destinations were aligned with intermodal transfer facilities to provide realistic routes and to ensure that route selection did

17

not favor one mode over another. We evaluated a number of disaggregation methods to provide a refined geographic location for flows. For origination and destination pairs (O/D pairs) outside of California (the “remainder of state” locations of CFS and FAF2), we distributed flow volume to major cities in the state not explicitly identified as Combined Statistical Areas and Metropolitan Statistical Areas (CSAs/MSAs). For O/D pairs with an origination or destination in California, these data were disaggregated based on population. Table 2 summarizes the disaggregation approaches, and Appendix B provides more detail on these disaggregation methods as well as how O/D pair locations are aligned with intermodal transfer facilities.

Table 2. Approaches to disaggregate flow data

Approach Within CA Outside CA Datasets 1 Same as approach for “outside CA” Distribution from CFS O/D

pairs; facilities located in major cities for CSAs; for “remainder of” we distributed to other large cities in the region equally; identification of other cities was somewhat arbitrary; destination at intermodal facilities in the cities OR at retail locations within the city.

CFS

2 Distribution by county in CA based on population of the county; destinations are determined by selecting an intermodal facility that is in the largest city within each county, or a warehouse or retail center within the largest city within each county if no intermodal facility exists.

Same as approach 1 CFS

3 Distribution by incorporated city within the LA/LB region (only) based on population to demonstrate; outside of LA/LB we apply approach #2; destinations in incorporated cities would be at intermodal facilities OR retail locations if no intermodal facilities exist.

Same as approach 1 CFS

4 Distribution from Cambridge Systematics disaggregation of the FAF2 dataset; destinations are identified as in approach #2 to identify destination locations for network modeling.

Distribution from Cambridge Systematics FAF2; destination based on approach 1.

Cambridge Systematics/FAF2

The different methods provide substantially similar results. For the case study, ARB proposed to use approach #4, as it provides the resolution we need with recently-available data outside of California, and appropriate disaggregation within California. As GIFT is data independent, it was straightforward to

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19

3.1 DataSourcesFor this Case Study, the international and domestic container traffic associated with each of the three port regions was obtained from two sources. The first source of data was the California Commodity Origin-Destination Database Disaggregation technical memorandum produced by Cambridge Systematics, Inc. for the California Department of Transportation and California Air Resources Board (ARB). These data were used to obtain freight distribution patterns for goods movement through California, which was then used as a proxy for the containerized goods movement distribution. This distribution was combined with the second data source -- the inbound and outbound container data for the ports of interest from the Army Corps of Engineers -- to estimate the container traffic associated with the ports. This process of obtaining port generated containerized traffic from freight distribution figures has been explained in detail in Appendix F: Creation of Origin and Destination and Volume Flow Model. This section describes data sources used for the Task 5 Case Study.

3.1.1 CambridgeSystematicsOrigin‐DestinationDatabaseThe Cambridge Systematics Origin-Destination (O/D) Database disaggregates the Freight Analysis Framework 2.2 (FAF2) data at the county level into a new O/D database. The FAF2 data is a freight database that provides estimates of commodity flows and transportation activity among states, metropolitan regions and international gateways. It is built from publicly available statistics such as the Commodity Flow Survey (CFS) and other sources highlighted on the FAF homepage (http://ops.fhwa.dot.gov/freight/freight_analysis/faf/index.htm).

Cambridge Systematics used principles of regression analysis in disaggregating the freight flow at the regional level to that at the county level. The freight traffic tonnage was estimated at the county level by forming regression models with explanatory variables such as industry employment, population and other factors that affect the production or consumption of a particular commodity in a county. For the counties in California, the tonnage values were adjusted for modal accessibility. The resultant database thus provides freight flow statistics by commodity and by mode, from and to the counties within the state of California. On the recommendation of ARB, the Cambridge Systematics O/D database was used to determine freight movements. The use of this data set also demonstrates the flexibility of GIFT in handling alternate sources of data. For details on CFS and the Cambridge Systematics FAF2 methodology, refer to Appendix F and Appendix G.

3.1.2 PortContainerDataThe second source of data utilized in the Case Study was the number of containers handled by the ports of interest. Data were obtained from the Waterborne Commerce Statistics Center, maintained by the Army Corps of Engineers (ACE) (Army Corps of Engineers, 2003). Figure 13 shows the freight flow conventions for the Case Study.

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qual to 1 standof 48,000 lbs

from the custr can enter intemissions calc

m of the total Dotal inbound frhe total Domeinbound/outbl the freight mistics for the ted containers worder to main

Total Outbound toPort TEUs1

1,835,51

(Total for LALB

454,07

672,97

rttons03.htm).

dard 20 ft lens.)

tom evaluatorto GIFT overculator to com

Domestic freight to the estic Inboundound and the

movement. Ththree port regwere conside

ntain consisten

Total InboundDestinatiTEUs1

9

A-B)

6,13

(Total fo

78 54

71 56

.

ngth by 8 ft w

r based on therall emissionsmpute emissio

d

hese ions, red ncy

to ion

34,033

or LA-LB)

45,868

65,352

width

e type rates ons

21

rates for specified fuels, engines, and operating parameters (see Appendix E for a description of the calculations) For reference purposes, this report restates vehicle assumptions and the network attributes that existed for Task 4, and were used for the Case Study in this report.

3.2.1 Emissionrates

3.2.1.1 TruckAssumptionsA Class 8 heavy-duty vehicle (HDV) that met model year (MY) 1998-2002 emissions standards was assumed to be carrying two TEUs weighing a total of 20 tons. The fuel economy of the vehicle was assumed to be 6.0 miles per gallon. Furthermore, the emission factors associated with the truck operation were assumed to be 6.06 grams of NOx per brake horsepower-hour (gNOx/bhp-hr) and 0.139 grams of PM10 per brake horsepower-hour (gPM10/bhp-hr). The emission factor values were sourced from Table B-5 and Table B-8 of Appendix B of the Carl Moyer Program Guidelines Handbook (California Air Resources Board, Part IV- Appendices, 2008).

3.2.1.2 RailAssumptionsTwo Tier-1 locomotives, each powered by a 4,000 hp motor, were assumed to be hauling a 100 well-car load, with each well-car carrying an equivalent of 4 TEUs at 10 tons per TEU. This amounts to a total of 4,000 tons of shipment. An average speed of 25 miles per hour was assumed over the entire rail network. The engines were assumed to be operating at an average efficiency of 35% and an average load factor of 70%. The emission factors associated with the rail were based on Tier 1 levels and assumed to be 6.3 gNOx/bhp-hr and 0.275 gPM10/bhp-hr. These values were sourced from Table B-18a of Appendix B of the Carl Moyer Program Guidelines Handbook (California Air Resources Board, Part IV- Appendices, 2008).

3.2.1.3 ShipAssumptionsMost of the O/D pairs in the Case Study do not allow for potential water routes, but some could, and the GIFT Model can evaluate the potential for waterways to serve goods movement for coastal regions in so-called "Short-Sea Shipping." The GIFT Model used vessel characteristics for the prototype short-sea vessel “Dutch-Runner” - a 3,070 hp container vessel with a capacity of 221 TEUs, with average payload of 10 tons/TEU (total of 2210 tons of freight). The engine was considered to be operating at 40% efficiency with an average load factor of 80%. Rated speed (i.e., design speed) of the vessel was approximated to be 13.5 statute miles per hour. The ship operates at the maximum allowable emissions standards for NOx (5.4 g/bhp-hr) and PM10 (0.15 g/bhp-hr) – in other words, meeting current regulations and not adjusted for emissions control standards that are pending.

3.2.1.4 FuelAssumptionsThe assumed fuel for the model evaluation study is on-road diesel fuel with energy content of 128,450 Btu/gallon, a mass density of 3,170 grams/gallon, and a carbon fraction of 86%. We applied this assumption to all modes, acknowledging that residual fuels and various quality distillate fuels vary somewhat. At the scale of this Case Study, the differences are smaller than the variability in other assumptions, but future analyses could use GIFT to model various fuels in terms of a low-carbon fuel standard or other environmentally beneficial fuel alternatives – either by mode or across modes.

The aforementioned figures gave a resultant output of 830 gCO2/TEU-mile for truck, 320 gCO2/TEU-mile for rail, and 410 gCO2/TEU-mile for ship. Thus, the most carbon-intensive mode of freight transport in this case is truck, followed by the container ship, and then rail (Figure 14).

Figure 14

3.2.2 AWhile thesegments associatedfacilities, associated(Figure 15

Figure 15

The princcalculate eoperationaoccurs at t

4. Emissions

Assumptionse GIFT emissibased on veh

d with the morepresented b

d with the net5).

5. Representi

iples behind temissions froal attributes othe intermoda

intensity for

sforIntermions calculatohicle type, a seovement of coby a hub-and-twork segmen

ing Intermod

the emissionsom transportatof the port equal facilities an

r different mo

odalTransfor computes theparate emiss

ontainer by ca-spoke modelnts of the thre

dal Facilities

s estimates fortion modes. Duipment. In rnd the spokes

22

odes

fershe emissions sions calculatoargo handling , have environe different mo

r cargo handlDifferences exreality, the tra are a proxy f

associated wor was develoequipment atnmental attribodes of transp

ling equipmenxist in the assansfer of goodfor the movem

with each of thoped to compt the ports. Thbutes similar port – road, ra

nt are the samsumptions regds from one mment. In the h

he network pute the emisshe intermodalto those ail and water

me as those usgarding the mode to anothhub-and-spok

sions l

sed to

her ke

23

model, the movement of the containers from one mode to another is modeled through artificial spokes (Figure 15 ). Thus, the Road Spoke represents the transfer of goods between the road network and the intermodal facility. Similarly, the Rail Spoke and the Water Spoke model the movement of goods between the facility and the rail network, and between the facility and the waterways network, respectively.

When estimating emissions at the facilities, the spokes were assumed to accumulate part of the emissions involved in a mode-to-mode transfer. For example, the total CO2 emissions generated in moving a container between the road network and the rail network would be spread across the road spoke and the rail spoke. Standard CO2 emission rates for the road spoke were calculated as the average of the CO2 emissions accumulated when moving a container from the road network to the rail network and from the road network to the waterway network.

Our assumptions regarding the emissions intensity of the cargo handling activity at the facilities (or ports) led to the approximate estimations listed in Table 4.

Table 4. Intermodal Transfer Emissions

Spoke Type Grams of CO2 per TEU

Grams of NOx per TEU

Grams of SOx per TEU

Grams of PM10 per TEU

Road 9200 1035 6.2 31.5

Rail 4100 53 0.5 1.6

Ship 2500 42 0.3 2

In effect, the total accumulated emissions along a route consisting of an origin point and a destination point can be summarized by the following equation where Ep is the total emissions of pollutant p, TEi,p is the transfer facility emissions penalty at transfer facility i for pollutant p and is summed over all transfers i; lj is the length of segment j in miles; and EFj,p is the emissions factor for pollutant p and segment j in grams/TEU-mile.

Equation 1

, ∙ ,

The emissions counted on a per TEU-mile basis are obtained for the three different modes depending on the vehicle attributes specified by the user through the emissions calculator or user-entered emissions rates . When optimizing for a particular emission, the travel routes are so selected that the accumulated emissions are minimum.

3.2.3 TravelTimeWhen solving for routes under various scenarios, the accumulated travel time is calculated based on the allowable speed limits on the road, rail and water network segments. For the road segments, the allowable speed is based on the road class. The common speed values range from 25 mph to 65 mph, with 5 mph intervals. For the rail network, a constant speed of 25 mph is assumed throughout the network. In case of the waterways, a constant speed of 13.5 mph (~12 knots) was assumed for a radius distance of 20 km from the coastline, and 20 mph beyond that.

24

Apart from the speed being a determinant of the travel time, dwell nodes were included in the rail network to take into account the delays associated with the movement of freight through a rail yard located at a facility or port. For details, refer to the section 2.3.5. Time accumulation is not reported in this Case Study, but it serves as a constraint for the least-time routing solution.

The GIFT emissions calculator calculates emission values based on the assumption of constant average speed for rail and ship. The emissions calculated for truck use average fuel economy assumptions, and are not adjusted for emissions rate variation with speed or engine load. Moreover, this Case Study does not adjust for grade and power relationships in truck or rail, and does not consider localized maneuvering behavior by ships. The travel times calculated in GIFT are based on the speeds associated with the network segments. Thus, the emissions estimated for freight movement are an approximation or best estimate. Under other Case Study designs using the GIFT model, we can define our estimations for environmental, time, cost, and other attributes of freight transportation to meet those purposes.

4 CaseStudyResultsThe freight data on container traffic from/to the three ports on the West coast were imported into ArcGIS. Routes were then solved using GIFT for the origin-destination (O/D) pairs under two different scenarios – least-time and least-CO2. This section discusses the results of the two scenarios.

4.1 Least‐timeRouteEmissionsAs shown in Figure 16 and Figure 17, under the least-time scenario the majority of the container traffic from the ports of Los Angeles-Long Beach, Oakland and Seattle are concentrated along parts of interstates I-5, I-10, I-15, I-40, and I-90. In the least-time case, the freight is routed through the roadway network because of the higher speeds involved. In effect, a total of approximately 2.9 MMT of CO2 emissions are estimated to occur over the course of the year due to freight moving in and out of these three ports on the West coast. This is under the assumption that all the freight moves by truck. Table 5 lists the estimated emission figures for the various attributes of choice. Of these, the majority of the emissions (~79% of total) are due to traffic moving in and out of the port of Los Angeles-Long Beach. This estimation is supported by the fact that the said port is the biggest on the West coast and one of the biggest in the U.S.

Figure 166. Container Traffic fromm Ports (Leas

25

st-time Scenaario)

Figure 17

Table 5. L

Emission

Attribute

CO2

NOx

SOx

PM10

7. Container

Least-time R

n

es

Total EmissionFrom Allport Traffic (MT)

2,885,36

55,51

15

1,42

Traffic to Po

Route Emissio

ns l

Total EmPort (M

Port of LA-LB

60 1,707,51

13 33,11

51 9

23 84

orts (Least-ti

ons

missions FroMT)

Port of OAKLA

10 102,

16 2,

91

49

26

ime Scenario

m Traffic fro

ANDPort oSEAT

,759 144

,277 2

7

60

o)

om Totaltowar

of TLE

Port LA-L

4,708 597,6

2,859 11,0

8

74 2

l Emissions Frds Port (MT

of LB

Port of OAKLA

680 206

013 3

28

281

From TrafficT)

fAND

Port SEAT

6,560 12

3,798

10

96

c

of TTLE

26,143

2,450

7

63

Figure 18The majorDesert airmajority oregion areFigure 18proportionlist of the

Figure 18

4.2 LeIn the casthe low emdistributioterms of sby a modaemissionsair basins

shows the dirity of emissir basins. This of the freight e correlated w shows estimnal distributioCO2 emission

8. Air Basin

east‐CO2Roe of the least-missions invoon is similar tsavings in emal switch froms can be seen pin California

istribution of ons are concefinding is suptraffic to be c

with the amounated CO2 emion of NOx, SOns by air basi

Emissions (L

outeEmis-CO2 scenarioolved with moto the least-timissions, it is e

m truck to traiprominently f

a, when comp

the freight traentrated withipported by thconfined withnt of freight tissions by air Ox and PM10 eins can be fou

Least-time sc

sionso, most of theoving freight bme scenario, aestimated thatin. (See Tablefor air basinsared with the

27

affic emissionin the South C

he maps in Fighin these regiotraffic movingbasin, it can

emissions fromund in Table 8

cenario)

e freight was rby train (Figuas evident by t a total of 59e 7 for emissi. Figure 21 sh

e visualization

ns by air basinCoast, San Jogure 16 and Fons. Thus, it cg within that be consideredm these good8.

routed througure 14). The pthe maps in F% reduction iions reductionhows how then show in Fig

ns, for the leaaquin Valley

Figure 17, whican be seen thregion. Whiled as a proxy f

ds movements

gh the rail netwpattern of the Figure 19 andin CO2 emissn comparisone emissions regure 18.

ast-time scenaand the Moja

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work becausefreight

d Figure 20. Iions is achiev

n). This changeduce across t

ario. ave

in a

ete

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n vable ge in the

Figure 19

9. Container Traffic fromm Ports (Leas

28

st-CO2 Scenaario)

Figure 20

Table 6. L

Emission

Attribute

CO2

NOx

SOx

PM10

0. Container

Least-CO2 R

n

es

Total EmissionFrom Allport Traffic (MT)

1,182,7

13,6

5

Traffic to Po

Route Emissio

ns l

Total EPort (M

Port of LA-LB

764 694,997

628 7,917

22 13

574 335

orts (Least-C

ons

Emissions FroMT)

Port of OAKLAN

7 45,3

7 5

3

5

29

CO2 Scenario

om Traffic fr

ND Port ofSEATT

337 56

597

1

24

o)

rom Total towar

f TLE

Port oLA-L

6,556 248,0

640 2,8

1

27 1

Emissions Frds Port (MT

of LB

Port of OAKLA

31 87

20 1

4

20

From Traffic T)

AND Port SEAT

7,227 5

1,078

2

44

of TTLE

50,616

576

1

24

Figure 21

4.3 CoFigure 22moving frleast-CO2

reductionsSouth Coaincurred temissionsrequire inspeeds; aldriving timhere, GIFemissionsthe emissiscenario bThis findipolicy dec

1. Air Basin E

omparison shows the amreight by train route solutios from systemast, San Joaquthe most of ths reduction in ncreased travelternatively, ifme, these diffT allows the

s. Another poions along thebecause of theing stresses thcisions.

Emissions (L

nofEmissimount by whin instead of trons, this reprem improvemenuin Valley an

he freight emithe most emi

el time if the rf long-haul truferences may comparison o

oint of note is e North Centre increased frhe importance

Least-CO2 sce

ionsacrosich the CO2 emruck. Given thsents an idealnts. Maximu

nd Mojave Dessions in the lissions-intensrailroad netwoucking with sbe much sma

of trade-offs wthat while th

ral Coast and eight traffic be of geospatia

30

enario)

sScenariomissions reduhis Case Studlized (that is,

um CO2 emissesert. These tleast-time scesive regions. ork average spsingle drivers aller. Althougwith respect tohe CO2 emissi

the South Cebeing routed tal attributes of

osuce across thedy with strict c

a, bounded) sions are reduthree regions enario. Thus, Of course, thpeed (25 mph requires rest

gh the differeno the travel tiions are reducentral Coast inthrough the raf freight emis

e air basins inconstraints foscenario for p

uced along thewere also thea modal shift

his reduction ih) is slower tht hours for evence in travel time when optced across moncrease in casailroads withissions and how

n California, wor least-time apotential CO2

e air basins ofe ones which t of freight leain emissions mhan the road ery 10 hours otime is not listimizing for ost of Californse of the leastin these regiow it can infor

when and 2

f

ads to may

of sted

nia, t-CO2 ns. rm

Figure 22

Table 7. E

EmissionAttribute

CO2

NOx

SOx

PM10

2. Emission V

Emissions Co

n e

LSE

Variations by

omparison fo

Least-time Scenario TotEmissions (M

2,8

y Air Basin

or Entire Ro

tal MT)

LeaScenEm

85,360

55,513

151

1,423

31

utes of All O

ast-CO2 nario Total issions (MT)

1,182,7

13,6

5

O-D Pairs

)

Total EReduct

764

628

22

574

Emission tion (MT)

1,702,596

41,885

129

849

Total EmiReductionpercent)

6 59

5 75

9 85

9 59

ission ns (in

9.01%

5.45%

5.43%

9.66%

32

Table 8. Emissions by Air Basin

Air Basin Total Least-time Scenario CO2 Emissions (MT)

Total Least-CO2 Scenario CO2 Emissions (MT)

Difference in CO2 Emissions due to Modal Shift (MT)

Percent Change

South Coast 375,866 149,421 226,445 -60%

San Joaquin Valley 178,572 58,690 119,882 -67%

Mojave Desert 120,951 60,908 60,043 -50%

San Francisco Bay 67,983 31,173 36,810 -54%

Salton Sea 48,900 41,672 7,228 -15%

Sacramento Valley 34,912 16,948 17,964 -51%

San Diego County 24,044 3,471 20,573 -86%

South Central Coast 14,986 17,164 (-2,178) 15%

Northeast Plateau 8,644 3,994 4,650 -54%

Mountain Counties 6,536 3,517 3,019 -46%

North Central Coast 3,100 6,240 (-3,140) 101%

North Coast 814 376 438 -54%

Great Basin Valleys 480 345 135 -28%

Lake County 36 17 19 -53%

Lake Tahoe 23 22 1 -4%

Total in-state 885,847 393,958 491,889 -56%

Note: Positive difference corresponds to negative percent change; both represent CO2 reductions.

5 CaseStudyDiscussionThe Case Study provides two primary insights. First, the Case Study quantifies port-related intermodal goods movement through the state of California and beyond. Second, the idealized use of least-CO2 routing constraints illustrates how emissions savings can be achieved through modal shifts. Both of these insights have relevance for consideration of system-wide improvements that may achieve energy savings, CO2 reductions, and associated benefits for air quality.

The California Greenhouse Gas Emissions Inventory developed by ARB reports that an estimated 26.9 MMT CO2 were emitted on average from heavy-duty diesel vehicles during the years 2002- 2004. These inventories are available from http://www.arb.ca.gov/cc/inventory/inventory.htm and http://www.arb.ca.gov/cc/inventory/data/forecast.htm). The Case Study estimates CO2 emissions to be approximately 2.89 MMT CO2 from the three West Coast port container traffic using the least-time

33

scenario (which comprises mostly trucks), for the same period (Table 5). If we assume that onroad heavy-duty diesel activity is primarily devoted to freight transport, the GIFT model estimates in-state CO2 emissions of port-related goods movement are about 11 percent of California CO2 from goods movement. This result is expected given that emissions estimated through GIFT only consider (loaded) containerized freight moving in and out of the three major ports on the West coast. Also, as shown in Table 9 our assumption of 10 tons of cargo per TEU means that we are estimating emissions for, on average, about 9 percent (by weight) of the total goods moving in and out of the three port regions. This may be expected given that containerized intermodal payloads are less densely packed than bulk goods. The difference between CO2 (and energy used) and amount of goods moved could be larger a) if the average weight per TEU is less than 10 tons; and b) if repositioning movements of empty containers were included.

Table 9. Comparing port containers and freight tonnage

LA-LB OAKLAND SEATTLE Total

Inbound Summary Destination Inbound TEUs1 A 6,134,033 565,352 545,868 7,245,253 Port TEU tons2 B(=A*10) 61,340,330 5,653,520 5,458,680 72,452,530 Region Tons From Region3 C 345,566,070 47,178,970 111,289,750 504,034,790 Percent of total inbound tonnage D(=B/C) 18% 12% 5% 14%

Outbound Summary Destination Outbound TEUs1 A 1,835,519 672,971 454,078 2,962,568 Port TEU tons2 B(=A*10) 18,355,190 6,729,710 4,540,780 29,625,680 Region Tons to Region3 C 381,499,940 55,553,670 202,376,070 639,429,680 Percent of total outbound tonnage D(=B/C) 5% 12% 2% 5%

Bidirectional Summary Total TEUs (Inbound + Outbound) A 7,969,552 1,238,323 999,946 10,207,821 Port TEU Tons Total B(=A*10) 79,695,520 12,383,230 9,999,460 102,078,210 Region Tons Total C 727,066,010 102,732,640 313,665,820 1,143,464,470 Total TEU Tons as Percent of Region D(=B/C) 11% 12% 3% 9%

1. Port container data (from US Army Corps of Engineers) 2. Assumed 10 tons per TEU 3. Cambridge Systematics database

Case study findings can also be discussed in the context of the Climate Change Scoping Plan of ARB. The ARB scoping plan recommends in measure T-6 that goods movement can achieve a total reduction of 3.5 MMT CO2 through adoption of system efficiency improvements (California Air Resources Board, AB 32 Scoping Plan Document, 2010). Our estimation of a total reduction of approximately 1.7 MMT of CO2 occurs through a nationwide modal shift of West Coast port-generated goods movement; within the state air basins, this reduction is near 0.5 MMT CO2. Of course, this assumes that all port-related TEUs currently move via truck; this Case Study did not adjust for the amount currently moving via rail, but produced two bounding cases (least-time and least-CO2.). Moreover, the port-related mode shift assumptions in this Case Study could be complemented or substituted by similar mode shifts for goods moving to and from other California destinations and origins. The point is that if goods movement system improvements could facilitate mode shift of this order, then between 14% and ~50% of the T-6 Scoping Plan goal could be achieved.

6 SummaryandConclusions This project further developed the GIFT model and demonstrated its configuration and use for evaluating tradeoffs among attributes of goods movement in the State of California. The model can be used to

34

evaluate least ‘cost’ transportation routes for single or multiple origin-destination pairs. The model includes the ability to optimize transportation for energy, environmental, economic, time-of-delivery, distance, and other attributes. This project involved collecting and implementing data obtained specific to the state of California, as well as steps to validate the model.

We demonstrated the model using California-specific inputs through a Case Study focused on CO2 emissions from goods movement of containers moving through the major California ports. The Case Study concentrated on exploring the least-time v. least-CO2 emissions of goods movement, but other opportunities exist to expand this tradeoff set in future work. Significant reductions in CO2 emissions are possible through intermodal changes and other energy-efficiency measures that would support the Air Resources Board goods movement goals. The final results of the case study (discussed in Sections 4 and 5) provide boundaries for potential CO2 emissions reductions in the goods movement sector for the state.

7 RecommendationsBeyond the Case Study results themselves, this project has demonstrated the feasibility and usefulness of GIFT and its California-specific data and configuration as an important analytical and planning tool for California decision makers. Although the Case Study focused solely on demonstrating emissions tradeoffs between least-time and least-CO2 routing, we recommend that future work entail analysis based on economic and other California specific attributes.

Future work also can involve migration of GIFT to a web-based environment so that California decision makers would have access to the model through the Internet. Some work related to this migration is ongoing through other project work being conducted by our research team, and leveraging that work with additional California based analyses is conceivable.

35

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Hawker, J. S., Falzarano, A., Ketha, S., Korfmacher, K., Winebrake, J., & Zilora, S. (2007). Intermodal Transportation Network Custom Evaluators for Environmental Policy Analysis. Paper presented at the ESRI International User Conference.

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US Army Corps of Engineers. 2007 Waterborne Commerce of the United States (WCUS). http://www.ndc.iwr.usace.army.mil/wcsc/webpub/webpubpart-4.htm

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US Army Corps of Engineers. (2008). Port Terminology. http://www.iwr.usace.army.mil/ndc/wcsc/webpub08/pdfs/Terminology.pdf

US Census Bureau. Geographic Areas Reference Manual. http://www.census.gov/geo/www/GARM/Ch8GARM.pdf

US Census Bureau. (1994). Geographic Areas Reference Manual. http://www.census.gov/geo/www/GARM/Ch9GARM.pdf

Vanek, F. M., & Morlok, E. K. (2000). Improving the energy efficiency of freight in the United States through commodity-based analysis: justification and implementation. Transportation Research Part D: Transport and Environment, 5(1), 11-29.

Winebrake, J. J., Corbett, J. J., & Meyer, P. (2007). Energy Use and Emissions from Marine Vessels: A Total Fuel Life Cycle Approach. Journal of the Air and Waste Management Association, 57, 102-110.

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Glossary

ARB California Air Resources Board

ArcCatalog A product from ESRI (see http://www.esri.com/) to build and manage geospatial information system databases

ArcGIS A Geographic Information System (GIS) commercially available from ESRI (see http://www.esri.com/)

ArcToolbox A set of geospatial data processing and management tools available as part of the ArcGIS product suite.

bhp Brake horse-power – a unit of measurement of power

BNSF Burlington Northern Santa Fe Railways

BTS United States Bureau of Transportation Statistics (http://www.bts.gov/)

BTU British Thermal Unit – a standard unit of energy

C# A computer programming language provided by Microsoft and used to customize the ESRI ArcGIS product

CA California

CARB California Air Resources Board

CBSA Core-Based Statistical Area – An area defined by the U.S. Office of Management and Budget to identify core urban areas and adjacent areas

CCD Census County Division – a subdivision of a county with no minor civil division (MCD) or other governmental boundary (for census purposes)

CDP Census Designated Place -- concentrations of population that are identifiable by name but are not legally incorporated (see http

//www.census.gov/geo/www/cob/pl_metadata.html)

CFS Commodity Flow Survey (http://www.bts.gov/publications/commodity_flow_survey/)

CN Canadian National Railway

CO2 Carbon dioxide

CP Canadian Pacific Railway

CSA Combined Statistical Area – a United States census region combining metropolitan and micropolitan regions linked by commuting ties (see http://www.census.gov/population/www/metroareas/metrodef.html)

CSX A United States-based rail transportation company

DOT United States Department of Transportation

ERG Eastern Research Group, a collaborator in the GIFT research projects

FAF and FAF2 Freight Analysis Framework versions 1 and 2 (http://www.ops.fhwa.dot.gov/freight/freight_analysis/faf/)

FEC Florida East Coast Railway

g Grams

GATX A United States-based rail transportation company – often referred to as General American Transportation

Geodatabase A file system for storing and managing geospatial information (from ArcGIS)

GIFT Geospatial Intermodal Freight Transportation model

hp Horsepower – a unit of measurement of power (see bhp)

IMO International Maritime Organization

km Kilometers

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KCS Kansas City Southern Railway

KWh Kilowatt-hour

LA Los Angeles, California

LA/LB Los Angeles/Long Beach – referring to the port complex near San Pedro, California

m meters

MCD Minor Civil Division. A census area defining a subcounty area such as a town or township

MSA Metropolitan or Micropolitan Statistical Area– a county or region census area containing a substantial urban area and its adjacent communities (see http://www.census.gov/population/www/metroareas/aboutmetro.html)

mol Mole – a unit of measure of the physical quantity of a gas

MY Model Year

NS Norfolk Southern Railway

NTAD National Transportation Atlas Database (available from the United States Bureau of Transportation Statistics at http://www.bts.gov/help/national_transportation_atlas_database.html)

O/D or O-D Origination / Destination – points marking the beginning and end of a transportation route

ObjectID A number uniquely identifying a segment in a transportation network file of ArcGIS

ppm Parts per million

RIT Rochester Institute of Technology

SOx Sulfur oxides

Shapefile A file format for representing geospatial points, polylines, and polygons

SourceID I number identifying a portion of a geospatial information system’s file set (identifies a shapefile in a geodatabase in the ArcGIS system)

STEEM Ship, Traffic, Energy and Environmental Model, a characterization of international ocean-going ship traffic (see http

//coast.cms.udel.edu/NorthAmericanSTEEM/)

TEU Twenty-foot equivalent unit – a standard measure for shipping containers

A measure of capacity for shipping containers

TRANSFLO An intermodal transloading provider in the United States – A subsidiary of CSX Corporation

UD University of Delaware

UP Union Pacific Railway

U.S. United States

USACE United States Army Corps of Engineers

Visual Basic A computer programming language provided by Microsoft and used to customize the ESRI ArcGIS product

VOC Volatile organic compounds WCSC Waterborne Commerce Statistics Center (see http

//www.iwr.usace.army.mil/ndc/wcsc/pdf/wcusnatl04.pdf)

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APPENDIX A: Data Summary Sheets for Mobile Sources

Data Summary Sheets

General data category: Highway Vehicle Fleet

Data title: Review of the US DOE Heavy Vehicle Technologies Program-Summary

Data source: National Academy of Sciences

Year released: 2000

Brief summary of the value of the data for the Cal GIFT Model: Lists truck fuel use at 4mil bbl/day for cars, 4.5 mil bbl/day for Class 1 & 2 trucks, and 3 mil bbl/day for Class 3-8 trucks. Estimates that trucks will consume twice as much fuel as cars by 2020.

Recommends implementation of a program to introduce technologies for trucks in order to reduce emissions and become more fuel efficient. Fuel efficiency improvement methods such as improving aerodynamics, use of lightweight materials, and decreasing roll resistance on trucks are recommended.

Limitations: No tangible data is provided in this document for use in the model.

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Data Summary Sheets

General data: Yard Trucks

Data title: Cargo Handling Equipment Yard Truck Off-Road Emission Testing

Data source: CARB

Year released: N/A

Brief summary of the value of the data for the Cal GIFT Model: CARB has a program to obtain information on baseline emissions and control strategies for port and intermodal rail yard trucks. Tested six yard trucks: 3 in use mechanically controlled off-road engines (1997, 2000, 2001 MY), 1 electronically controlled off-road engine (2004 MY), 1 electronically controlled on-road engine (2004 MY) and 1 LPG engine. All were Cummins 8 mode test cycle 5.9L or 8.3L. Emission control strategies suggested are yard trucks with on-road certified engines, use of alternative fuels such as propane or natural gas, use of emulsified diesel, or installments of after-treatments.

Emission testing performed using constant volume sampling for PM emissions. Weighting factors applied are listed in the document Yard truck test matrix developed to represent the makeup of the existing fleet, evaluate

effectiveness of using emulsified diesel, compare currently available and certified on and off-road engines, and test alternatively fueled propane yard trucks.

Results of testing on emissions for NOX, PM and THC are discussed Provides modal data

Limitations: Data limited to yard trucks for Port of Los Angeles and Port of Long Beach

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Data Summary Sheets

General data category: Truck / Rail

Data title: Evaluating the public investment mix in US Freight Transportation Infrastructure

Data source: Michael F. Gorman, Elsevier

Year released: 2005, 2007

Brief summary of the value of the data for the Cal GIFT Model: Discusses how 25% of the truck freight could be handled at 25% lower cost if rail infrastructure to support it existed. An additional 80% reduction in social costs could be achieved through this modal conversion. Discusses modal efficiency comparison and shipper modal choice behavior. Looks at both private and social costs. Provides intermodal rail operating costs on a per ton mile basis, and truck operating costs. Estimates costs for infrastructure investment costs. Discusses variables such as congestion costs, social costs, pollution. Provides data to support these discussions.

Limitations: Does not discuss specific locations.

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Data Summary Sheets

General data category: Highway vehicle fleet

Data title: Institute for Trade and Transportation Studies, Volume I, Issue 5

Data source: Institute for Trade and Transportation Studies (ITTS)

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: ITTS is working with the American Transportation Research Institute (ATRI) is developing truck speed performance measurements along the Alliance Region’s major corridors, and ATRI, in cooperation with the Federal Highway Administration (FHA), is developing a database of average truck speeds along the nations highway system.

Limitations: This document is more of a newsletter, and is discussing the I-10 corridor specifically.

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Data Summary Sheets

General data category: Highway vehicles, rail, transfer facilities

Data title: A Geographic Information System Framework for Transportation Data Sharing

Data source: Kenneth Ducker, Allison Butler, Center for Urban Studies, Portland State University

Year released: 2000

Brief summary of the value of the data for the Cal GIFT Model: This paper develops a framework and principles for sharing transportation data to achieve more accurate presentation of transportation data. Paper talks about the Geographic Information Systems Transportation (GIS-T) model. This is an intermediate form from which databases to support applications can be generated.

Limitations: Differences in definitions of basic transportation entities in data models could result in obstacles in sharing data.

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Data Summary Sheets

General data category: Cargo handling equipment

Data title: Preliminary Analysis of GHG Emissions from Cargo Handling Equipment at Ports During Extended Idling

Data source: CARB

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: Discuss fuel consumption, fuel costs, emissions. Idling time was estimated and then used to calculate the associated emissions and fuel consumption due to idling. This was then projected to annual emission estimates for PM, NOX and CO2 for year 2007.

Limitations: Exhaust temperature data gathered from port terminals only. Data was not collected for yard trucks. A very small sample was used, would need a larger sample in order to be more representative of the fleet.

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Data Summary Sheets

General data category: Highway Vehicle Fleet

Data title: Assessment of Out of State Truck Activity in California

Data source: Nicholas Lutsey, Institute of Transportation Studies, University of California at Davis

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model:

Data collected on truck activity in California: o Trips/year into California o Days spent in California o Amount of fuel trucks are carrying o Travel patterns such as point of entry into California

General Truck sample statistics were collected: o Type of truck o Registration o Days of operation o Miles driven/year o Fuel capacity

Limitations: Since data is not kept on interstate trucks’ activity while in California, results from truck activity registered outside California collected during a survey were extrapolated to estimate California’s in-state truck activity.

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Data Summary Sheets

General data category: Marine, rail, and highway

Data title: Annual Energy Outlook, 2008

Data source: Department of Energy, Energy Information Administration

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: Provides national level energy/fuel usage and fuel cost data.

Limitations: Little information that is specific to California.

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Data Summary Sheets

General data category: Railroad and highway vehicles

Data title: GREET

Data source: Argonne National Laboratory

Year released: data downloaded October 2009

Brief summary of the value of the data for the Cal GIFT Model: Provides generic emission factors

Limitations: Emission factors from GREET need to be adjust to better match locomotives (line-haul and yard) and to account for the introduction of California-specific emission and fuel standards .

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Data Summary Sheets

General data category: Railway

Data title: Class 1 Railroad Statistics

Data source: Association of American Railroads (AAR).

Year released: November 18, 2008.

Brief summary of the value of the data for the Cal GIFT Model: Provides excellent general information about Class 1 railway that can be used to quality check operational assumptions made for the CalGift model.

Limitations: The information is aggregated to the national level, California specific data are not provided. For interstate railroad shipment, this data source may be useful to quantify fuel consumption rates, mileage, and some information about the composition of the national line-haul fleet.

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Data Summary Sheets

General data category: Railroad and highway activities

Data title: Analysis of Transportation Options to Improve Fuel Efficiency and Increase the Use of Alternative Fuels in Freight and Cargo Movement in the California/Mexico Border Region

Data source: California Energy Commission

Year released: August 2008

Brief summary of the value of the data for the Cal GIFT Model: Provides insight into cross border freight shipments with Mexico, including information about existing highway vehicle activities and emission factors. Issue related to infrastructure limitations and appropriate potential control options are also present which can be incorporated into the model limit projected activity and emissions.

Limitations: The study focused on crossborder shipments with Mexico, and did not consider freight originating in other states. Also because Mexican ports currently have a limited amount of containership traffic and the rail links with California are few and traffic is limited, most of the intermodal freight is associated with highway truck transfers.

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Data Summary Sheets

General data category: Facility highway, locomotive and nonroad

Data title: Statewide Strategies to Reduce Locomotive and Associated Rail Yard Emissions.

Data source: California Environmental Protection Agency, Air Resources Board (CARB)

Year released: December, 2006; and updated in September 2009.

Brief summary of the value of the data for the Cal GIFT Model: This reference provides detail insight into appropriate control options that can be specifically applied to California intermodal facilities to reduce risk from yard related emission sources. Some of the information provided is also useful for port facilities.

Limitations: The study only provides recommended control options, while actual penetration and control effectiveness are needed to adjust facility emissions to accurately represent actual emissions.

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Data Summary Sheets

General data category: Railway, marine vessel, and higway-trucks

Data title: FY 2004-2006 Carl Moyer Program, Multi District Projects

Data source: California Environmental Protection Agency, Air Resources Board (CARB),

Year released: March 22, 2008

Brief summary of the value of the data for the Cal GIFT Model: The information provided in the Carl Moyer Program summaries is important to ensure that transfer and port facility emissions are appropriate adjusted to reflect re-engining of vessels, application of idle reduction devices, and use of electric, hybrid and alternative fuel yard and cargo handling equipment.

Limitations: In order to account for control options promoted by initiatives such as the Carl Moyer Program data is required for multiple years to accurately assess changes in the equipment fleet.

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Data Summary Sheets


Data title: Rail Short Haul Intermodal Corridor Case Studies

Data source: Casgar, C. S., DeBoer, D. J. et al.

Year released: 2003

Brief summary of the value of the data for the Cal GIFT Model: The Federal Railroad Administration’s Office of Policy and Program Development sponsored the development of this document in the interest of information exchange. The objective of this report is to provide an industry context for public officials who are interested in rail short haul intermodal corridors and to offer a template for analyzing related costs and benefits. The cost data included in this study may of value for the model to assess costs associated with construction of corridors that speed up short haul operations. The study also includes many actual case studies that can be used to calibrate the model.

Limitations: The case studies provided include only cities on the east coast and need to be assessed carefully to determine their suitability for west coast corridors.

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Data Summary Sheets


Data title: Railroad and Locomotive Technology Roadmap

Data source: Argonne National Laboratory, Center for Transportation Research (CTR)

Year released: December 2002

Brief summary of the value of the data for the Cal GIFT Model: Report provides excellent summary information concerning via able emission reduction approaches. Report also includes considerable amount of information about the national railroad fleet that can be used as surrogate data or to validate assumptions made in the model.

Limitations: Some of the data needs to be updated to more accurately reflect recent economic changes. The study does not include data specific to California.

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Data Summary Sheets

General data category: Marine vessel

Data title: Modal Comparison of Domestic Freight Transportation Effects on The General Public

Data source: Center for Ports and Waterways, Texas Transportation Institute (TTI)

Year released: March 2009

Brief summary of the value of the data for the Cal GIFT Model: This study provides excellent summary information about inland waterway traffic.

Limitations: Because very little containerized shipment are associated with barge operations, most of this report have little value to the model.

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Data Summary Sheets

General data category: Marine vessel and railways

Data title: Air Pollution Emission Inventory Guidebook-2009

Data source: CORINAIR, European Monitoring and Evaluation Programme / European Environmental Agency

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: This report provides emission factors and fuel consumption information for marine vessels and railway operations. These data may be useful to develop validate assumptions made in the model.

Limitations: None of the emission factor or fuel consumption data is specific for California emission sources.

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Data Summary Sheets

General data category: Railways

Data title: Energy Efficiency Technology for Railroads

Data source: International Union of Railways (IUR)

Year released: Continually being updated

Brief summary of the value of the data for the Cal GIFT Model: This data set provides one of the most comprehensive inventories of control options for railways. It includes data on anticipated emission reductions, fuel savings, economic and social costs and benefits.

Limitations: Much of the information provides is highly technical and will require considerable amount of work to incorporate into any model. The data provided does not indicate technologies that are currently in use in California, additional research will be needed to accurately assess the penetration of these control options in the states

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Data Summary Sheets

General data category: Railways

Data title: Impact of Technology on Rail Network Capacity

Data source: TTCI

Year released: April 2009

Brief summary of the value of the data for the Cal GIFT Model: This study evaluates the impact that car ordering has on intermodal shipments .

Limitations: The information provided in this report is not appropriate for use in the model

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Data Summary Sheets

General data category: Railway- highway drayage trucking

Data title: Exploring a Green Alternative for Container Transport

Data source: Presentation provided to the Port of Los Angeles Harbor Commission by S. Roop and J. Lavish

Year released: 2006

Brief summary of the value of the data for the Cal GIFT Model: This presentation provides opetational details related to implementation of maglev shuttle service as a replacement for drayage trucks

Limitations: At this stage the data provided in this presentation is not relevant for inclusion into the model.

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Data Summary Sheets

General data category: on-road and railway

Data title: Annual Energy Outlook 2008

Data source: Annual Energy Outlook 2008

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: This data set includes energy consumption and cost data for current and projected years.

Limitations: The data provided does not differentiate fuel consumption or cost specific for the state of California. The cost data would have to be adjusted to account for the fuels that are specifically used in the state. The other data elements may have value to quality check assumption made about fuel usage.

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Data Summary Sheets

General data category: On-road and railway

Data title: North American Transborder Freight Data

Data source: U.S. Department of Transportation, Bureau of Transportation Statistics.

Year released: August 2009.

Brief summary of the value of the data for the Cal GIFT Model: These data quantify cross border activities between Mexico and California. The data set is particularly useful to evaluate commodity exchanges.

Limitations: The study focused on crossborder shipments with Mexico, and did not consider freight originating in other states. Also because Mexican ports currently have a limited amount of containership traffic and the rail links with California are few and traffic is limited, most of the intermodal freight is associated with highway truck transfers.

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Data Summary Sheets

General data category: On-road, marine, and railway

Data title: North American Freight Transportation: Trade with Canada and Mexico

Data source: U.S. Department of Transportation. Bureau of Transportation Statistics

Year released: 2006

Brief summary of the value of the data for the Cal GIFT Model: This study provided useful data concerning trading patterns between the U.S. , Mexico and Canada.

Limitations: The information provided in this report does note traffic through California ports of entry, but these data are dated, while Transborder Freight Data, noted above provides similar details and is frequently updated.

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Data Summary Sheets

General data category: Railroad, marine vessel, and on-road trucks

Data title: Transportation Statistics Annual Report, 2008,

Data source: U.S. Department of Transportation. Bureau of Transportation Statistics,

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: The study includes national fuel price data, emissions data and fuel consumption data by mode, which may be of value to the model if data are need to gap fill missing data elements.

Limitations: The fuel price data may be useful, but other data sources may be more recent, for example the latest railroad fuel cost data is 2006, no data were available for 2007or 2008.There is some information about intermodal transfer in the study, but nothing specific for California

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Data Summary Sheets

General data category: On-road, marine, and railway

Data title: State Transportation Statistics 2008.

Data source: U.S. Department of Transportation. Bureau of Transportation Statistics,

Year released: 2008

Brief summary of the value of the data for the Cal GIFT Model: This study provides California specific freight data that can be used to calibrate the model to insure that the traffic patterns reported by the model are similar to those found in this DOT report.

Limitations: The data used in the study range between 2004 and 2006 and it is not always easy to differentiate what port of the on-road and rail data relate to intermodal shipments.

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Data Summary Sheets

General data category: Marine vessels

Data title: Particulate emissions from commercial shipping: Chemical, physical, and optical properties.

Data source: Lack, D. A., J. J. Corbett, et al. J. Geophys. Res. 114(D00F04).

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model:.

This report characterizes particulate emissions on the basis of chemical, physical, and optical properties from commercial vessels. Observations during the Texas Air Quality Study/Gulf of Mexico Atmospheric Composition and Climate Study 2006 field campaign provide chemical and physical characteristics including sulfate (SO4 ) mass, organic matter (OM) mass, black carbon (BC) mass, particulate matter (PM) mass, number concentrations (condensation nuclei (CN) > 5 nm), and cloud condensation nuclei (CCN). Optical characterization included multiple wavelength visible light absorption and extinction, extinction relative humidity dependence, and single scatter albedo (SSA). The global contribution of shipping PM was calculated to be 0.90 Tg aâˆ’1, in good agreement with previous inventories (0.91 and 1.13 Tg aâˆ’1 from Eyring et al. (2005a) and Wang et al. [2008]). Observed PM composition was 46% SO4 2âˆ’, 39% OM, and 15% BC and differs from inventories that used 81%, 14%, and 5% and 31%, 63%, and 6% SO4 2âˆ’, OM, and BC, respectively. SO4 2âˆ’ and OM mass were found to be dependent on fuel sulfur content as were SSA, hygroscopicity, and CCN concentrations. BC mass was dependent on engine type and combustion efficiency. A plume evolution study conducted on one vessel showed conservation of particle light absorption, decrease in CN > 5 nm, increase in particle hygroscopicity, and an increase in average particle size with distance from emission. These results suggest emission of small nucleation mode particles that subsequently coagulate/condense onto larger BC and OM. This work contributes to an improved understanding of the impacts of ship emissions on climate and air quality and will also assist in determining potential effects of altering fuel standards.

Limitations: Though the study does not focus on California vessels, the data can be used to update and supplement the marine vessel emission factor data.

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Data Summary Sheets

General data category: Marine Vessels

Data title: The effect of Mg-based additive on aerosol characteristics in medium-speed diesel engines operating with residual fuel oils

Data source: Lyyranen, J., J. Jokiniemi, et al. Journal of Aerosol Science 33: 967-981

Year released: 2002


Aerosol measurements were carried out to determine particle formation and characteristics produced in a 4-stroke, turbo-charged 1 MW diesel engine operating with high ash-content heavy fuel oil with and without a Mg-based additive. The mass size distributions are bimodal (modes at 0.1 and 10 um, aerodynamic size) without additive and have three modes (additional mode at 2 um) with the additive. It was found that the 2 um mode was generated by magnesium together with some vanadium, nickel and sulfur. The primary particles are formed by nucleation of the volatilized fuel oil ash species that further grow by condensation and agglomeration. The 10 um mode particles are mainly re-entrained from deposits and fuel residue particles of different sizes. Primary particle size is about 40–100 nm as observed in the SEM and TEM micrographs. It appears that the 9ne particles (0:1 um mode) are more spheroidal and catenulate with the additive than without.


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Data Summary Sheets


Data title: Aerosol Characterisation In Medium-Speed Diesel Engines Operating With Heavy Fuel Oils.

Data source: Lyyranen, J., J. Jokiniemi, et al. Journal of Aerosol Science 30(6): 771-784.

Year released: 1999


Aerosol measurements were carried out in medium-speed diesel engines to determine the aerosol characteristics and formation in four-stroke diesel engines equipped with turbocharger(s) burning heavy fuel and high ash-content heavy fuel oil. The mass size distributions are bimodal with a main mode at 60Ð90 nm and a second mode at 7Ð10 km. The small mode particles are formed by nucleation of volatilized fuel oil ash species, which further grow by condensation and agglomeration. The large mode particles are mainly agglomerates of different sizes consisting of the small particles. The number size distributions peak at 40Ð60 nm, as also observed in the SEM micrographs. Agglomerates consisting of these primary spherical particles are also found. The TEM micrographs reveal that these particles consist of even smaller structures. Based on the mass and elemental size distributions evidence of high volatility of the fuel oil ash was found. The main effect on the aerosol size distributions was caused by the engine type and fuel oil properties.


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Data Summary Sheets


Data title: A Critical Review of Ocean-Going Vessel Particulate Matter Emission Factors

Data source: Sax, T. and A. Alexis

Year released: 2007

Brief summary of the value of the data for the Cal GIFT Model: California Air Resources Board staff has conducted an exhaustive evaluation of available data to assess ocean-going vessel particulate matter (PM) emission factors. The goals of this assessment were to compile available testing data, analyze potential confounding relationships in the data, and assess emission factors. Our analysis identified no significant difference between emission factors for auxiliary and main engines and between emission factors and load factor, installed power, or model year. CARB found PM emission factors for vessels operating on heavy fuel oil at 2.5% sulfur content were statistically significantly higher (1.5 g/KW-hr) than vessels operating on distillate fuel (0.3 g/KW-hr). While they expected to identify a clear relationship between fuel sulfur content and PM emission factors, our analysis identified only a weak relationship. A future sulfur emission control area may be defined across North America with a 1.5% fuel oil sulfur content limit. Based on the weak relationship between PM emission factors and fuel sulfur content identified in this paper, they estimate the PM emission factor at 1.5% fuel sulfur would be reduced by about 30% to 1 g/KW-hr. Future research and additional testing is necessary to improve PM emission factor measurements from large vessel engines, and to better assess the relationship between PM emission factors and fuel sulfur content.

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Data Summary Sheets


Data title: Class I Railway Company R-1 Reporting Forms, 2009.

Data source: U.S. Department of Transportation, Federal Railroad Administration

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: These data are reported to the FRA by individual railway companies, including details about their fleet of locomotives, cars, fuel consumption, and national cargo traffic. These data are useful to characterize the national fleet of individual locomotives and can be used to gap fill missing line haul data or to validate data used to populate the state line haul operations.

Limitations: The data are not disaggregated at the state level, such that the data are only useful for the GIFT model where it is appropriate to gap fill with national level data, such as with long haul operations. The data also has some value as a reference point for quality assurance checks on the data used to populate the model to ensure that the State data are similar to the national data.

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Data Summary Sheets


Data title: Technical Highlights: Emission Factors for Locomotives (EPA420-F-09-025).

Data source: U.S. Environmental Protection Agency, Air and Radiation, Office of Transportation and Air Quality.

Year released: April 2009.

Brief summary of the value of the data for the Cal GIFT Model: The data in this report quantify the regulatory emission standards for locomotives. These standards vary by year of original manufacture and whether the locomotive has been remanufactured. The study clearly shows assumptions that were made in developing the emission factors associated with these locomotive standards. The factors included in this report can be used directly in the GIFT model or they can be used as validation checks on more detailed locomotive specific factors to ensure that the factors used are compliant with the appropriate standard.

Limitations: These standards do not reflect changes to the fleet that operate in California - for example California probably has the largest population of hybrid and genset locomotives operating relative to other states. These locomotives significantly out perform the EPA standards.

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Data Summary Sheets


Data title: Characteristics of the Existing U.S. Locomotive Fleet

Data source: U.S. Environmental Protection Agency, National Vehicle and Fuel Emissions Laboratory,

Year released: 28 February 2007

Brief summary of the value of the data for the Cal GIFT Model: This study provides summary information about the national fleet of locomotives. Some of the data are useful to quantify general characteristics such as typical horsepower ranges.

Limitations: The information in this study do not represent individual state fleets, such that the data are only useful for the GIFT model where it is appropriate to gap fill with national level data, such as with long haul operations. The data also has some value as a reference point for quality assurance checks on the data used to populate the model to ensure that the State data are reasonable relative to available national data.

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Data Summary Sheets

General data category: Railway and Marine vessel

Data title: Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder ( EPA420-R-08-001a)

Data source: U.S. Environmental Protection Agency/ Assessment and Standards Division, Office of Transportation and Air Quality

Year released: May 2008

Brief summary of the value of the data for the Cal GIFT Model: This study provides considerable information about the national marine and locomotive fleet, including details about fleet composition and, fuel consumption, and cargo traffic patterns. These data are useful to characterize the national fleet of individual locomotives and can be used to gap fill missing line haul data or to validate data used to populate the state line haul operations.

Limitations: It should be noted that vast majority of containerships involved in California's intermodal cargo traffic are equipped with category 3 propulsion engines (cylinder volume great than 30 liters) and are therefore not included in this evaluation.

The locomotive data included in this study are not disaggregated at the state level, such that the data are only useful for the GIFT model where it is appropriate to gap fill with national level data, such as with long haul operations. The data also has some value as a reference point for quality assurance checks on the data used to populate the model to ensure that the State data are similar to the national data.

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Data Summary Sheets

General data category: Railways and Marine vessels

Data title: The State of U.S. Railroads, Rand Corporation Technical Report, 2008.

Data source: Weatherford, Brian; Willis, H.H.; and Ortiz, D.S., Rand Corporation

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: This Study includes summary data about the current state of the railroad sector focusing on existing fleet of locomotives, rail cars and infrastructure relative to growing demand. This study includes some unusual but useful data such as:

Ton-mile per track mile Miles of track per locomotive Track maintenance costs Social cost associated with congestion Average tons per train Average length of haul Average speed for intermodal traffic by railway company Average dwell time per company

These data may be useful in gap filling missing data in the model or to calibrate the model to ensure that the operational components are reasonable.

Limitations: The information included in this study is not disaggregated at the state level, such that the data are only useful for the GIFT model where it is appropriate to gap fill with national level data. The data also has some value as a reference point for quality assurance checks on the data used to populate the model to ensure that the State data are similar to the national data.

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Data Summary Sheets

General data category: Marine, Railway, On-road trucking.

Data title: Great Lakes Maritime Research Institute, Intermodal Freight Transport in the Great Lakes: Development and Application of a Great Lakes Geographic Intermodal Freight Transport Model,

Data source: Winebrake, J. J.; Corbett, J.J.; Hawker, S; and Kormacher, K. for the Great Lakes Maritime Research Institute.

Year released: October 31, 2009

Brief summary of the value of the data for the Cal GIFT Model: This is a recent version of the GIFT model that was developed for the Great Lakes Area and includes a lot of functionality that would be appropriate for the Cal GIFT model.

Limitations: This version of the model has been created for the Great Lakes area and therefore the data it contains would have to be adjusted to accurately reflect operations in California.

75

Data Summary Sheets


Data title: In-use gaseous and particulate matter emissions from a modern ocean going container vessel

Data source: Agrawal, Harshit; Malloy, Quentin G.J.; Welch,William A.; Miller, J. Wayne; Cocker, David R. III; Atmospheric Environment, Volume 42, Issue 21,

Year released: July 2008

Brief summary of the value of the data for the Cal GIFT Model: This paper provides the emission measurements of gases, particulate matter (PM), metals, ions, elemental and organic carbon, conducted from the main engine of an ocean going PanaMax class container vessel, at certification cycle and at vessel speed reduction mode, during actual operation at sea. The composition of PM, from main engine is dominated by sulfate and water bound with sulfate (about 80%of total PM) and organic carbon constitutes about 15% of the PM. Sulfur, vanadium and nickel are the significant elements in the exhaust from the engine running on the HFO. At the point of sampling 3.7–5.0% of the fuel sulfur was converted to sulfate. These results can be incorporated into the Cal GIFT marine vessels emission factors directly as they relate to vessel directly involved in intermodal shipments.

76

Data Summary Sheets

General data category: Marine Vessel

Data title: Characterization of particulate matter and gaseous emissions from a large ship diesel engine

Data source: Jana Moldanova, Erik Fridell, Olga Popovicheva, Benjamin Demirdjian, Victoria Tishkova, Alessandro Faccinetto, and Cristian Focsa

Year released: 2008

Brief summary of the value of the data for the Cal GIFT Model: In this study, the composition of exhaust from a ship diesel engine using heavy fuel oil (HFO) was investigated onboard a large cargo vessel. The emitted particulate matter (PM) properties related to environmental and health impacts were investigated along with composition of the gas-phase emissions. Mass, size distribution, chemical composition and microphysical structure of the PM were investigated. The PM composition was dominated by organic carbon (OC), ash and sulphate while the elemental carbon (EC) composed only a few percent of the total PM. Increase of the PM in exhaust upon cooling was associated with increase of OC and sulphate. Hazardous constituents from the combustion of heavy fuel oil such as transitional and alkali earth metals (V, Ni, Ca, Fe) were observed in the PM samples. Measurements of gaseous composition in the exhaust of this particular ship showed emission factors that are on the low side of the interval of global emission factors published in literature for NOx, hydrocarbons (HC) and CO. These results need to be evaluate to determine if they should be incorporated into the Cal GIFT marine vessels emission factors.

Limitations: The study focus on heavy oils, which may be appropriate for the operations on the open seas, but may not be appropriate for operations in California state waters. Further evaluation is needed to determine how best to use these data.

77

Data Summary Sheets


Data title: Emission measurements from a crude oil tanker at sea

Data source: Agrawal, H., W. A. Welch, et al.; Environmental Science and Technology 42(19): 7098-7103

Year released: 2008

Brief summary of the value of the data for the Cal GIFT Model: This study provides emission factors for the main propulsion engine (ME), auxiliary engine (AE) and an auxiliary boiler on a Suezmax class tanker while operating at sea. The data include criteria pollutants (carbon monoxide, nitrogen oxides, sulfur oxides, and particulate matter), a greenhouse gas (carbon dioxide), speciated hydrocarbons, and a detailed analysis of the PM into its primary constituents (ions, elements, organic, and elemental carbon). The vessel burned two fuels: a heavy fuel oil in the ME and boiler and a distillate fuel in the AE. This article also provides emission factors for selected polycyclic aromatic hydrocarbons, heavy alkanes, carbonyls, light hydrocarbon species, metals, and ions for the ME, AE, and the boiler. These results need to be evaluated to determine if they should be incorporated into the Cal GIFT marine vessels emission factors.

Limitations: The study focus on heavy oils, which maybe appropriate for the operations on the open seas, but may not be appropriate for operations in California state waters. Furthermore tanker operations may be significantly different than containership operations. Additional evaluation is needed to determine how best to use these data.

78

Data Summary Sheets


Data title: North American Port Container Traffic 2006.

Data source: American Association of Port Authorities

Year released: 2007

Brief summary of the value of the data for the Cal GIFT Model: This report provides a summary of container traffic for all major ports in North America.

Limitations: The data only covers 2006, U.S. Army Corps of Engineers entrance and clearance data provides more detailed information about California ports

79

Data Summary Sheets


Data title: Airborne Toxic Control Measure For Auxiliary Diesel Engines and Diesel-Electric Engines Operated On Ocean-Going Vessels with in California Waters and 24 Nautical miles Of The California Baseline

Data source: California Air Resources Board

Year released: June 29, 2009

Brief summary of the value of the data for the Cal GIFT Model: The model should be adjusted to reflect the application of this regulation on container ships that operate with in 24 nautical miles of the California coast.

80

Data Summary Sheets


Data title: Fuel Sulfur and Other Operational Requirements for Ocean-Going Vessels Within California Waters and 24 Nautical Miles of the California Baseline




81

Data Summary Sheets


Data title: Airborne Toxic Control Measure for Fuel Sulfur and Other Operational Requirements for Ocean-Going Vessels within California Waters and 24 Nautical Miles Of The California Baseline




.

82

Data Summary Sheets


Data title: Ocean Going Vessel Emission Control, Technology Matrix,

Data source: California Air Resource Board

Year released: October 2002

Brief summary of the value of the data for the Cal GIFT Model: This matrix provides a comprehensive summary of available emission control options and provides a percent reduction for PM and NOx. These data can be easily incorporated in to the GIFT model to estimate emission reductions associated with the application of these technologies on container ships.

Limitations: The study only includes PM and NOx, additional data will be needed to account for reductions of other pollutants and impacts on fuel usage.

Also control technologies that do reduce emissions from other pollutants were not included in the matrix.

This assessment evaluated individual control technologies, Additional research will be needed to account for emission impacts of application of multiple control technologies.

83

Data Summary Sheets


Data title: Analysis of Transportation Options to Improve Fuel Efficiency and Increase the Use of Alternative Fuels in Freight and Cargo Movement in the California/Mexico Border Region.

Data source: California Energy Commission

Year released: August 2008

Brief summary of the value of the data for the Cal GIFT Model: This report, which is part of a larger California Energy Commission project on energy issues in the California/Mexico border region, discusses opportunities to reduce energy consumption and emissions associated with the movement of goods across the border. The study includes a description of the transportation infrastructure, trade patterns, and mode shares in the border region. Opportunities to use alternative and reformulated fuels and advanced vehicle technologies for highway, rail, marine, and aviation modes are discussed as well. Information in this study may be of value to account for cross border cargo flow and define technologies that are appropriate for this unique component of California cargo traffic.

Limitations: The data presented in this study are specific for the California/Mexico border regional and may have limited value for the rest of the state.

84

Data Summary Sheets

General data category: Marine vessel, railway, and on-road trucks

Data title: Modal Comparison of Domestic Freight Transportation Effects on The General Public

Data source: Center for Ports and Waterways, Texas Transportation Institute (TTI),

Year released: March 2009

Brief summary of the value of the data for the Cal GIFT Model: This study provides summary data for a wide range of intermodal components, which can be used to validate assumptions made in the model. These data elements included, highway truck trailer capacity and fuel efficiencies for highway trucks and railways.

Limitations: Because the focus is on barge activities - some of the data are not meaningful for

85

Data Summary Sheets


Data title: 2005-2006 British Columbia Ocean-Going Vessel Emission Inventory.

Data source: Chamber of Shipping

Year released: January 2007

Brief summary of the value of the data for the Cal GIFT Model: This report presents the results from the emissions inventory of ocean-going (deep-sea) vessels in B.C.waters, for the period from April 1, 2005 to March 31, 2006. Emission estimates are provided for ten criteria air contaminants and for greenhouses gases (GHGs). The report also contains a variety of compiled statistics on vessel behaviour. This study relied on two key data sources: (1) high-resolution Coast Guard VTOSS Track data with detailed information on vessel location and speed, sampled on a 3-7 minute interval, (2) survey data on vessel characteristics for each vessel making a port call in B.C. during the study period. Emissions estimates were compiled for the entire study area, which includes all inland and territorial waters along the B.C. coast, the US and Canadian portions of the Strait of Juan de Fuca, and oceanic waters extending 50 nautical miles offshore. There is little data in this study that would directly relate to California ports, but it does provide information that would be useful to validate assumptions made in the GIFT model.

Limitations: This study does not include information related to California ports.

86

Data Summary Sheets


Data title: Ship Emissions Inventory - Mediterranean Sea: Final Report

Data source: CONCAWE (implemented by Entec UK, Limited)

Year released: 2007

Brief summary of the value of the data for the Cal GIFT Model: This emission inventory of marine vessel activities in the Mediterranean does include containerships. The value of this data set for GIFT is limited and there are other studies that are more relevant for California, which should be preferred over this study.

Limitations: This study does not contain information directly related to California intermodal ship traffic. The emission factors included in this study are similar to those found in other sources.

87

Data Summary Sheets


Data title: Allocation and Forecasting of Global Ship Emissions

Data source: Corbett, James; Wang, C.; Winebrake, J.J.; and Green, E.

Year released: January 11, 2007.

Brief summary of the value of the data for the Cal GIFT Model: This report presents global inventories of emissions from international shipping. The study provides a clear summary of recent work, evaluates growth in international shipping, includes a spatially allocated global ship emission inventory (0.1 X 0.1 degree lat/long) and evaluates BAU forecast of fleet energy and emissions trends. The insight provided in this study can be used in the GIFT model to quantify or validate eastern trade routes.

Limitations: The projections data presented in this report were developed prior to the current economic down turn and need to be revised with revised growth projections if needed.

The study also lays out procedures to adjust the ICOADS vessel to address sampling bias, by trimming over-reported vessels, using multiple year data and by weighting ship observations with ship installed power.

88

Data Summary Sheets


Data title: Air Pollution Emission Inventory Guidebook-2009.

Data source: CORINAIR, European Monitoring and Evaluation Programme / European Environmental Agency,

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: The Guide summarizes recommended criteria, metal HAPs and PAH emission factors and provides emission reduction for commercially available control devices.

Limitations: The data in this report is appears to be an aggregation of data provided in the ENTEC and SEPA marine vessel emission factor studies. The SEPA data is preferred as it provides more disaggregated emission factors that can be more easily tailored to account for the types of containerships that operate in California waters.

89

Data Summary Sheets


Data title: Quantification of Emissions from Ships Associated with Ship Movements between Ports in the European Community: Final Report

Data source: European Commission developed by Entec UK Limited

Year released: July 2002

Brief summary of the value of the data for the Cal GIFT Model: This study has been conducted by Entec UK Ltd on behalf of the European Commission, with sub-consultants IVL of Sweden, to address the following key tasks:

Quantify ship emissions of SO2, NOx, CO2 and hydrocarbons in the North Sea, Irish Sea, English Channel, Baltic Sea, Black Sea and Mediterranean, as well as quantify in-port emissions of these pollutants plus particulate matter; Determine emissions for all vessels as well as separately for each vessel

type and flag state (Registered in the European Community or outside) Estimation of the effects of the MARPOL Agreement and additional future

scenarios upon emissions, principally sulphur dioxide and particles, in the North Sea and Baltic Sea and other European seas; Undertake a market survey of low sulphur marine distillates; and Investigate the feasibility of ships storing and using multiple grades of marine

distillates.

The main value of this study for the GIFT model are the emission factors used in this study. The authors have done an excellent job compiling the most recent test data of marine vessel data and converting these data into usable emission factors.

Limitations: The SEPA factors appear to be an update and expansion of the EC/Entec factors which includes metal HAPs, dioxin, HCB, and GHG pollutants and would therefore be preferred.

90

Data Summary Sheets


Data title: Assessment of Transport Impacts on Climate and Ozone: Shipping, Atmospheric Environment

Data source: Eyring, V., I. S. A. Isaksen, et al.

Year released: 2009 (in press)

Brief summary of the value of the data for the Cal GIFT Model: This study presents an assessment of the contribution of gaseous and particulate emissions from oceangoing shipping to anthropogenic emissions and air quality. Using the global temperature change potential (GTP) metric indicates that after 50 years, the net global mean effect of current emissions is close to zero through cancellation of warming by CO2 and cooling by sulfate and nitrogen oxides. The study includes a variety useful data about fuel consumption that can be used to calibrate the model or QA the models fuel consumption data.

91

Data Summary Sheets


Data title: Lloyd’s Registry of Ships

Data source: Fairplay

Year released: 2009 (updated quarterly)

Brief summary of the value of the data for the Cal GIFT Model: Lloyds printed the first Register of Ships in 1764 in order to give both underwriters and merchants an idea of the condition of the vessels they insured and chartered: Since 1880, the Register, with information on all sea-going, self-propelled merchant ships of 100 gross tonnes or greater. Currently the registry is updated quarterly by the joint venture company of Lloyd's Register - Fairplay which was formed in July 2001 by the merger of Lloyd's Register's Maritime Information Publishing Group and Prime Publications Limited. The registry provides vessel specific characteristics data including: IMO identification number, call sign, vessel type, draft, length, dead weight tonnage, net tonnage, propulsion and auxiliary engine type, engine kW rating, stroke, cylinder diameter, engine speed, vessel maximum speed, etc. Individual vessels identified by AIS or US ACE entrance and clearance data can be linked to Lloyds data based on the vessels IMO numbers. Many of the other data elements (i.e., fuel type, propulsion and auxiliary engine type, engine kW rating, and engine speed) are critical for developing vessel specific emission factors using data sources such as SEPA. The cylinder volume can be calculated using the engine stroke and cylinder diameter, in order to determine which EPA regulatory group the vessel is associated with.

Limitations: Some of the data fields related to auxiliary engines is not well populated, and surrogates would have to be developed to fill missing data.

92

Data Summary Sheets


Data title: A Comparative Evaluation of Greenhouse Gas Emission Reduction Strategies for the Maritime Shipping and Aviation Sectors.

Data source: Hansen, Mark; Smirti, M.; and Zou, B.,

Year released: July 27, 2008

Brief summary of the value of the data for the Cal GIFT Model: This study provides a useful summary of strategies which can control GHG emissions from marine vessels. These strategies can be incorporated in to the GIFT model to evaluate the air quality impacts of different control scenarios relative to changes in traffic patterns.

Limitations: The control options evaluated in this study needed to be evaluated to identify those that specifically relate to the types of containerships that frequent California ports.

93

Data Summary Sheets

General data category: On-road trucking

Data title: Supply Chain Bottlenecks: Border Crossing Inefficiencies between Mexico and the United States; International Journal of Transport Economics XXXI(No.2).

Data source: Haralambides, H. E. and Londono-Kent, M. P.

Year released: 2004

Brief summary of the value of the data for the Cal GIFT Model: This study provides detailed insight into the process of moving cargo across the California/Mexico border. Such information can be incorporated into the GIFT model to quantify delays at the border.

94

Data Summary Sheets


Data title: Updated Study on Greenhouse Gas Emissions from Ships: Final Report Covering Phase 1 and Phase 2

Data source: International Maritime Organization (IMO)

Year released: 9 April 2009

Brief summary of the value of the data for the Cal GIFT Model: This study provides estimates of present and future emissions from shipping. Emissions from water-borne navigation into two primary categories: domestic and international, where “international waterborne navigation” is defined as navigation between ports of different countries. Emission estimates from domestic shipping and emissions from fishing are also included in this report. The study addresses greenhouse gases (CO2, CH4, N2O, HFCs, PFCs, SF6) and other relevant substances (NOx, NMVOC, CO, PM, SOx) that are defined in the terms of reference for this study. Annual inventories of emissions of greenhouse gases and other relevant emissions are provided for the study period of 1990 to 2007. The report also included an analysis of the progress in reducing emissions from shipping through implementation of MARPOL Annex VI. An analysis of technical and operational measures to reduce emissions in also include in this report along with analysis of policy options to reduce emissions. Scenarios for future emissions from international shipping are also evaluated in this study. There is also a section of the report that analysis the effect of emissions from shipping on the global climate. Lastly the study provides a comparison of the energy efficiency and CO2 efficiency of shipping compared to other modes of transport. The GHG and criteria emission factors presented in this report are the most comprehensive and update and should be used directly in the GIFT model.

Limitations: Some of the emission factor data is aggregated and does not account for containerships that operate in California state waters. In order to develop a comprehensive data set of emission factors, data from other studies will be needed to supplement the IMO emission factors for missing pollutants.

95

Data Summary Sheets


Data title: Particulate emissions from commercial shipping: Chemical, physical, and optical properties. J. Geophys. Res. 114(D00F04).

Data source: Lack, D. A., J. J. Corbett, et al.


Brief summary of the value of the data for the Cal GIFT Model: This study characterizes particulate emissions on the basis of chemical, physical, and optical properties from commercial vessels. Observations during the Texas Air Quality Study/Gulf of Mexico Atmospheric Composition and Climate Study 2006 field campaign provide chemical and physical characteristics including sulfate (SO4

2−) mass, organic matter (OM) mass, black carbon (BC) mass, particulate matter (PM) mass, number concentrations (condensation nuclei (CN) > 5 nm), and cloud condensation nuclei (CCN). The emission factors provided in this study should be evaluated for inclusion into the GIFT model.

96

Data Summary Sheets


Data title: The effect of Mg-based additive on aerosol characteristics in medium-speed diesel engines operating with residual fuel oils; Journal of Aerosol Science 33: 967-981.

Data source: Lyyranen, J., J. Jokiniemi, et al.

Year released: 2002.

Brief summary of the value of the data for the Cal GIFT Model: In this study aerosol measurements were carried out to determine particle formation and characteristics produced in a 4-stroke, turbo-charged 1 MW diesel engine operating with high ash-content heavy fuel oil with and without a Mg-based additive. The primary particles were formed by nucleation of the volatilized fuel oil ash species that further grew by condensation and agglomeration. The 10 μm mode particles were mainly re-entrained from deposits and fuel residue particles of different sizes. Fractionated PM emission factors provided in this study should be evaluated for inclusion into the GIFT model.

97

Data Summary Sheets


Data title: Particle Formation in Medium Speed Diesel Engines Operating With Heavy Fuel Oils; Journal of Aerosol Science 29(Supplement 1): S1003-S1004

Data source: Lyyranen, J., J. Jokiniemi, et al.

Year released: 1998.

Brief summary of the value of the data for the Cal GIFT Model: Aerosol measurements were carried out in this study of medium-speed diesel engines to determine the aerosol characteristics and formation in four-stroke diesel engines equipped with turbocharger(s) burning heavy fuel and high ash-content heavy fuel oil. The main effect on the aerosol size distributions was caused by the engine type and fuel oil properties. Fractionated PM emission factors provided in this study should be evaluated for inclusion into the GIFT model.

98

Data Summary Sheets


Data title: Comprehensive Simultaneous Shipboard and Airborne Characterization of Exhaust from a Modern Container Ship at Sea; Environmental Science and Technology,

Data source: Murphy, Shane M.; Agrawal, Harshit; Sorooshian, Armin; Padr, Luz T.; Gates, Harmony; Hersey, Scott; Welch, W.A.; Jung , H.; Miller, J. W.; Cocker, David R. III; Nenes, Athanasios; Jonsson , Haflidi H.; Flagan, Richard C. and Seinfeld, John H.

Year released: February 4, 2009

Brief summary of the value of the data for the Cal GIFT Model: This report focused on the chemical composition of particulate ship emissions. Emissions testing was implemented on the main propulsion engine of a Post-Panamax class container ship cruising off the central coast of California and burning heavy fuel oil. The mass spectrum of the organic fraction of the exhaust aerosol strongly resembled emissions from other diesel sources and appeared to be predominantly hydrocarbon-like organic (HOA) material. Emission test results provided in this study should be evaluated for inclusion into the GIFT model.

99

Data Summary Sheets


Data title: The present State of Marine Transports and Environmental Countermeasures in Japan; OECD/ITF Global Forum on Sustainable Development: Transportation and Environment in Globalizing World.

Data source: Okada, Hiroshi

Year released: November 22, 2008.

Brief summary of the value of the data for the Cal GIFT Model: This presentation included a selection of control technologies being implemented or being considered for implementation to address ship emissions in Japan. The technologies presented in this study would be appropriate for California marine vessel activities. This information could be included in the GIFT model to evaluate the air quality impact of different control scenerios.

Limitations: Some of the options presented here do not relate specifically to container ships, and the observations noted in this presentation may have to be evaluated for California ports and container ships that operate in California waters.

This assessment evaluated individual control technologies, additional research will be needed to account for emission impacts of application of multiple control technologies.

100

Data Summary Sheets


Data title: Exploring a Green Alternative for Container Transport, Presented to the Port of Los Angeles Harbor Commission.

Data source: Roop, S. and Lavish, J.

Year released: 2006

Brief summary of the value of the data for the Cal GIFT Model: This presentation includes a variety of control options specific to the Port of Los Angeles which could be included in the model to evaluate air quality impacts of different control scenarios.

101

Data Summary Sheets


Data title: Swedish Methodology for Environmental Data, Methodology for Calculating Emissions from Ships

Data source: Swedish Environmental Protection Agency (SEPA)


Brief summary of the value of the data for the Cal GIFT Model: This report derived emission factors for ships (> 100 Gross Register Tonnage). The study has focused on 28 different air pollutants, where the emission factors have been proposed as a function of engine and fuel type. For year 2002, the factors cover three operational modes (“at sea”, “maneuvering” and “in port”) and thereby take into account main engine and auxiliary engine emissions. In order to obtain representative and up-to-date emission factors for this application, “in-house” emission data and also published literature emission factor databases were assessed. Thus emission factors were derived from a database consisting of exhaust measurements from 62 ships involving 180 marine engines. The emission factors have been weighted to account for the proportion of the fleet using exhaust gas cleaning measures, age factors for fuel consumption and increased use of low-sulphur fuels. Since the number of measurement data available for the different pollutant emission factors varies considerably, an attempt was made to classify the factors after estimated uncertainty.

Limitations: These factors should be supplemented with more recently developed factors discussed in this report; specifically speciated PM factors.

102

Data Summary Sheets

General data category:

Data title: Evaluation of Low Sulfur Fuel Availability- Pacific Rim.

Data source: Starcrest for the Ports of Los Angeles and Long Beach

Year released: July 2005.

Brief summary of the value of the data for the Cal GIFT Model: This report evaluates the availability of lower sulfur fuels use in containerships calling on the Ports of Los Angeles and Long Beach, as well as operational issues, emissions benefits, and emission reduction costs associated with their use. Data in this study should be evaluated to better estimate the sulfur concentration of the fuel currently used by containerships that visit California ports to ensure that GIFT mode provides the most accurate sulfur emission estimates. The report also discusses the availability in the pacific of low sulfur fuels. This information is critical in order to adjust the sulfur emission in the model to better reflect compliance with international SECA regulations. Fuel sulfur concentration is also import in evaluating different control scenarios as high sulfur fuels can poison catalytic system.

Limitations: The data in this study is helpful to evaluate potential sulfur levels of fuel, but given the international nature of global cargo shipments and fuel cost constraints it is sometimes very difficult to predict fuel sulfur levels. It is recommended that this study we reviewed along with EPA fuel usage studies developed by RTI for the EPA's recent Category 3 vessel regulations.

103

Data Summary Sheets


Data title: Waterborne Commerce Statistics Center.

Data source: U.S. Army Corp of Engineers

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: The Waterborne Commerce Statistics Center's standard publications, Waterborne Commerce of the United States. Domestic and foreign vessel trips and tonnages by commodity for ports and waterways are covered in the Report. Foreign waterborne commerce between the U.S. and foreign countries are summarized by U.S. port, foreign port, foreign country, commodity group, and tonnage. Data summaries include origin to destination information of foreign and domestic waterborne cargo movements by region and state, and also waterborne tonnage for principal ports and state and territories. Internal waterway tonnage indicators are updated monthly on the NDC website. The report is issued in five parts (one to cover each coast and a national summary). Also available is The Public Domain Database which contains aggregated information of foreign and domestic waterborne cargo movements. Transportation Lines of the United States contains listings of domestic vessel operators, details their equipment and references their service areas. Most data are available in both hard copy and electronic form. Specialized data processing requests are considered on a case-by-case basis and are charged accordingly

Limitations: At this time, the Army Corps of Engineers are initiating a new electronic system of reporting system and there may be delays in getting 2009 data.

104

Data Summary Sheets

General data category: Marine vessel, railway, and on-road trucking

Data title: National Energy Modeling System. Annual Energy Outlook 2008: Report #:DOE/EIA-0383

Data source: U.S. Department of Energy, Energy Information Administration

Year released: April 2009.

Brief summary of the value of the data for the Cal GIFT Model: The Annual Energy Outlook 2009 (AEO2009) presents projections and analysis of US energy supply, demand, and prices through 2030. The projections are based on results from the Energy Information Administration's National Energy Modeling System. The AEO2009 includes the reference case which was updated this spring to reflect the provisions of the American Recovery and Reinvestment Act (ARRA) that were enacted in mid-February 2009. The need to develop an updated reference case following the passage of ARRA also provided the Energy Information Administration (EIA) with an opportunity to update the macroeconomic outlook for the United States and global economies, which has been changing at an unusually rapid rate in recent months. These data can be used in the GIFT model to project future fuel demand and baseline control scenerios.

Limitations: It should be noted that the data included in the AEO is national level data and may not reflect projected fuel demand or control levels specifically for California.

105

Data Summary Sheets

General data category: On-road trucking, rail and marine vessel traffic

Data title: Transborder Freight Data.

Data source: U.S. Department of Transportation, Bureau of Transportation Statistics.

Year released: Retrieved June, 2007

Brief summary of the value of the data for the Cal GIFT Model: The North American Transborder Freight Database, has been available since April 1993, and contains freight flow data by commodity type and by mode of transportation (rail, truck, pipeline, air, vessel, and other) for U.S. exports to and imports from Canada and Mexico. The database includes two sets of tables; one is commodity based while the other provides geographic detail. The purpose of the database is to provide transportation information on North American trade flows. This type of information is being used to monitor freight flows and changes to these since the signing of the North American Free Trade Agreement (NAFTA) by the United States, Canada and Mexico in December 1992 and its entry into force on January 1, 1994. The database is also being used for trade corridor studies, transportation infrastructure planning, marketing and logistics plans and other purposes. These data, specifically the Mexico/California data should be evaluated for incorporation into the GIFT model, this would allow the users to analyze cross-border movement of merchandise by all land modes and waterborne vessels.

106

Data Summary Sheets


Data title: Final Regulatory Support Document: Control of Emissions from New Marine Compression-Ignition Engines at or above 30 Liters per Cylinder, EPA420-R-03-004.

Data source: United States Environmental Protection Agency (2003),

Year released: January 2003.

Brief summary of the value of the data for the Cal GIFT Model: This document includes multiple other studies that quantify large vessel operations including underway and in-port activities, fuel usage, and emissions. The studies disaggregate vessel types to include containerships relative to five different dead weight tonnage size categories. There is a lot of information in this study that can be used directly in the model to gap fill missing data or as a reference point to quality check data used to populate the model.

Limitations: These documents primarily focus on national level activity, though California ports are specifically included in the in- and near- port components of this study; care should be taken in extrapolating the national data to represent California vessels and activities. Because the data is presented in a disaggregated format, it is possible to extract data appropriate for California.

107

Data Summary Sheets


Data title: Onroad Mobile Sources T6/T7 Heavy Duty Diesel Emission Factors for Calendar Year 2020

Data source: EMFAC 2009, Version 2.50.7a (EMFAC 2009)

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: The Emission FACtors (EMFAC) model is developed by the California Air Resources Board and used to calculate emission rates at the state, air district, air basin, or county level from on-road motor vehicles. EMFAC models six criteria pollutants (CO, NOx, TOG, SOx, Lead, and PM (either as PM2.5 or PM10); six priority mobile source air toxics (benzene, 1,3-butadiene, acetaldehyde, formaldehyde, acrolein, DPM) and CO2 emission factors. Emissions are calculated for twenty one different vehicles classes comprised of passenger cars, various types of trucks and buses, motorcycles, and motor homes. EMFAC contains default vehicle activity data, and the option of modifying that data, so it can be used to estimate a motor vehicle emission inventory in tons/day for a specific year, month, or season, and as a function of ambient temperature, relative humidity, vehicle population, mileage accrual, miles of travel and speeds. EMFAC2009 includes new data and methodologies regarding calculation of motor vehicle emissions and revisions to implementation data for control measures. EMFAC2009 includes updated data supporting new emission factors and speed correction factors for estimating emissions from heavy-heavy duty diesel trucks. The model includes modifications to the algorithms for inspection and maintenance as well as corrections for heavy-duty truck gas cap benefits from the inspection and maintenance program. Impacts of ethanol permeation and updates to fuel correction factors are included as well as revisions to particulate brake wear emissions. EMFAC2007 incorporates new temperature and humidity profiles. In addition to these changes, which impact emission factors for each area in California, EMFAC incorporates new mileage accrual rates and speed distributions, a redistribution of heavy-duty diesel truck vehicle miles traveled (VMT) and updated VMT for all vehicle classes.

Limitations: The Air Resource Board has not approved EMFAC 2009 for public release. ARB will be revising the EMFAC later this year, at which time updated factors can be provided.

108

Data Summary Sheets


Data title: International Maritime Organization Adopts Program to Control Emissions from Oceangoing Vessels, EPA-420-F-08-033.

Data source: U.S. Environmental Protection Agency

Year released: October 2008

Brief summary of the value of the data for the Cal GIFT Model: Currently the EPA is proposing rules that would comply with the IMO's Sulfur Emission Control Area (specifically regulations 13 and 14 and Appendix III of MARPOL Annex VI). It is important to track these developments to insure that the GIFT model accounts for the implementation of these fuel emission standards guidelines.

Limitations: Currently the EPA is setting forth a proposal to designate as an Emission Control Area specific portions of the coastal waters of the United States and Canada, in accordance with MARPOL Annex VI. Until the rule is promulgated, it is difficult to anticipate how this may affect containerships operating in California waters.

109

Data Summary Sheets


Data title: Boosting Energy Efficiency, Energy Efficiency Catalogue, Ship Power Research and Development.

Data source: Wartsila

Year released: February 3, 2009.

Brief summary of the value of the data for the Cal GIFT Model: Wartsila is one of the largest ship manufacturing firms in the world and this presentation provides a comprehensive summary of available and near-future marine control options. The presentation is provided in a format to clearly identify those technologies that apply to containerships. The fuel and emission reductions noted in the presentation can be applied to the GIFT model to evaluate the impact of different control scenarios.

Limitations: This assessment evaluated individual control technologies, additional research will be needed to account for emission impacts of application of multiple control technologies.

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Data Summary Sheets


Data title: Energy Use and Emissions from Marine Vessels: A Total Fuel Life Cycle Approach. Journal of the Air and Waste Management Association 2007, 57, 102-110.

Data source: Winebrake, J. J.; Corbett, J. J.; Meyer, P.,

Year released: 2007

Brief summary of the value of the data for the Cal GIFT Model: This study described the Total Energy & Emissions Analysis for Marine Systems (TEAMS) model. TEAMS can be used to analyze total fuel life cycle emissions and energy use from marine vessels. TEAMS captures "well-to-hull" emissions, that is, emissions along the entire fuel pathway, including extraction, processing, distribution, and use in vessels. TEAMS conducts analyses for six fuel pathways: (1) petroleum to residual oil, (2) petroleum to conventional diesel, (3) petroleum to low-sulfur diesel, (4) natural gas to compressed natural gas, (5) natural gas to Fischer-Tropsch diesel, and (6) soybeans to biodiesel. TEAMS calculates total fuel-cycle emissions of three greenhouse gases (carbon dioxide, nitrous oxide, and methane) and five criteria pollutants (volatile organic compounds, carbon monoxide, nitrogen oxides, particulate matter with aerodynamic diameters of 10 μm or less, and sulfur oxides). TEAMS also calculates total energy consumption, fossil fuel consumption, and petroleum consumption associated with each of its six fuel cycles. TEAMS can be used to study emissions from a variety of user-defined vessels. This paper provides example modeling results for three case studies using alternative fuels including a container ship. TEAMS data could be incorporated in the GIFT model to adjust the emissions factors to account for upstream emissions associated with the different fuel marine vessel used. The inclusion of such data would allow for a more comprehensive air quality impact assessment of different control strategies.

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Data Summary Sheets


Data title: MOVES Versus EMFAC: Comparison of Greenhouse Gas Emissions Using Los Angeles County

Data source: Bai, Song; Eisinger, Douglas S; and Niemeier, Debbie

Year released: 2009

Brief summary of the value of the data for the Cal GIFT Model: The U.S. Environmental Protection Agency is developing a new generation emission model, MOVES (Motor Vehicle Emission Simulator), to replace MOBILE6. MOVES changes the basis for mobile source emissions estimation from average speed to modal activity. This study examines differences in features, methods, and results between MOVES and EMFAC. Using a Los Angeles County, California application; two greenhouse gases, carbon dioxide (CO2) and methane (CH4); and two analysis years, 2002 and 2030 were considered. At the county level, for 2002 MOVES produced similar CO2 emissions, but only 42% of the CH4 emissions estimated by EMFAC; for 2030, MOVES produced 40% higher CO2 emissions and CH4 emissions were nearly double the estimates provided by EMFAC. Important contributing factors to these differences are the activity data and emission rates embedded in MOVES. The default vehicle activities indicated a younger fleet and higher miles traveled for light-duty trucks by 2030. The CO2 emissions differences between the two models appear to be mainly affected by the magnitude of forecasted vehicle miles traveled; CH4 emissions results tended to be most effected by the emission rates. EPA considers the underlying MOVES database for CO2 and CH4 emissions to be a draft and emissions results will likely change with upcoming model releases. Such studies further re-enforce the position that EMFAC data should be used in the GIFT model.

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APPENDIX B: Using the Model in Case Study

This appendix describes how to use the model in a case study. It describes specific steps to configure the model, determine freight flow based on differing optimization settings, and example results.

How to Run a Multiple OD-Pair Route Analysis in ArcGIS Network Analyst The model uses ArcGIS Network Analyst to solve routing problems for OD pairs across a multi-modal network and provides solutions as polyline features (routes). The resulting route solutions are represented by single polylines that include, for example, total time and total distance attributes associated with specific OD pairs but do not include the time and/or distance traveled on any given mode. By default, there is no way to determine how much of a solved route was traveled by ship, rail or road, which is crucial to understanding and assessing route solutions. To address this shortcoming, separate time and distance fields were added to the network dataset for each of the component feature classes and modes of transportation. The following fields were added to the eleven feature classes making up the network.

Rail_Miles Rail_Kilometers Rail_Hours Ship_Miles Ship_Kilometers Ship_Hours Road_Miles Road_Kilometers Road_Hours

The network dataset was then re-constructed adding the above fields as additional evaluator attributes to be accumulated when routes are solved. Using the enhanced network, time and distances are accumulated by mode for route solutions. The resulting routes have nine additional fields representing the accumulated time and distance traveled for each mode for all segments traversed by a given route. Summing the time and distance of each mode equals the total time and distance of the route.

The enhanced network provides total time and distance for each mode which collectively make up a specific route solution allowing for quick identification of the modes making up a route and their associated time and distance.

Further processing is required to determine the specific locations where any given mode is traversed and where mode changes occur. The solution developed to create unique IDs for each network segment for the dissolve process described below also allows for the mode of each segment to be identified by a numeric code contained in the DissolveID, making it possible to identify the mode of each segment making up a route. Thus, the DissolveID can be used to create visual representations of the modes traveled by any given route.

Importing Multiple OD Sets into Network Analyst (METHOD 1) We have developed two ways of importing lists of OD points for use in Network Analyst. The first involves creating an Excel spreadsheet with the following fields for each origin port.

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Name Lat Long RouteName Sequence

PORT OF LONG BEACH 33.7395700 -

118.2095000 Albany_Schenectady_Amsterdam NY CSA 1

Albany_Schenectady_Amsterdam NY CSA 42.6427100 -73.7481600

Albany_Schenectady_Amsterdam NY CSA 2


118.2095000 Anchorage_Alaska 1

Anchorage_Alaska 61.2224600 -

149.8879300 Anchorage_Alaska 2


118.2095000 Arkansas_Little Rock 1

Arkansas_Little Rock 34.6900700 -92.3261700 Arkansas_Little Rock 2

Within ArcGIS, click on Tools-Add XY Data to create an events theme of the OD Point data

Navigate to your Excel file and select the correct spreadsheet

X and Y fields should load correctly (LONG and LAT)

Edit the Coordinate System – Select – Geographic Coordinate System – North America – North American Datum 1983 (or whatever your XY coordinate units are)

Click OK

Select OK again to acknowledge the pop-up message. To create a shapefile or feature class from this events theme, right click and choose data export.

Activate the Network Analyst extension

Click on Network Analyst – New Route

Open the Network Analyst Window

Right click on Stops

Click Load Locations

Navigate to your XY Events theme (e.g., LALB) – Name and RouteName should fill in automatically

Click OK

The OD Pairs will load into separate two-point route sets, with the origin port listed first. This will result in a route originating from the port and ending at the paired destination.

Importing Multiple OD Sets into Network Analyst (METHOD 2)

The second method involves creating separate Excel spreadsheets for origins and destinations with the following fields for each file.

DESTINATIONS File

ORIGINS File

Name Lat Long Name Lat Long

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Albany_Schenectady_Amsterdam NY CSA 42.6427100 -73.7481600PORT OF LONG BEACH 33.7395700 -118.2095000

Anchorage_Alaska 61.2224600 -149.8879300 PORT OF SEATTLE 47.5877110 -122.3592180

Arkansas_Little Rock 34.6900700 -92.3261700 PORT OF OAKLAND 37.8215200 -122.3081000

Within ArcGIS, click on Tools-Add XY Data to create an events theme of the OD Point data

Navigate to your Excel file and select the correct spreadsheet X and Y fields should load correctly (LONG and LAT) Edit the Coordinate System – Select – Geographic Coordinate System – North America – North

American Datum 1983 (or whatever your XY coordinate units are) Click OK

Select OK again to acknowledge the pop-up message. Repeat these steps for the other spreadsheet, resulting in two events themes (origins and destinations). To create shapefiles or feature classes for these events themes, right click on each and choose data export.

Activate the Network Analyst extension

Click on Network Analyst – New Closest Facility Open the Network Analyst Window Right click on Facilities Click Load Locations Navigate to your XY Events theme for Origins (e.g., LALB) – Port name should fill in

automatically Click OK Right Click on Incidents Click Load Locations Navigate to your XY Events theme for Destinations (e.g., Albany_Schenectady_Amsterdam NY

CSA) – Destination name should fill in automatically Click OK Right Click on the Closest Facility Layer in the ArcMap Table of Contents Select Layer Properties Click the Analysis Settings Tab Select "Travel From:" as Facility to Incident

Routes will be generated from Facilities (Origins) to Incidents (Destinations) with the resulting route names including both Origin and Destination in one concatenated field when solved.

Creating Routes Optimizing on Various Impedance Attributes

To run a set of routes, click on the Route Properties icon located in the Network Analyst window

Under the General Tab, change the layer name (e.g. LALB HOURS) and add a description

Under the Analysis Settings Tab, select the impedance attribute (e.g. HOURS)

Under the Accumulation Tab, check the network accumulation attributes you wish to generate

Click Apply and OK

Click the Solve icon in the Network Analysts Toolbar

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NOTE: All GIFT Evaluator parameters are entered via the GIFT cost factor management and calculator tools except HOURS, KILOMETERS, and MILES. HOURS is an attribute field in each polyline network features and the DWELL TIME junction features. For linear features, HOURS is calculated by dividing the MILES attribute by the SPEED attribute for the road, rail, and waterway features. The transfer facility spoke features (road_spoke, rail_spoke, water_spoke) for the US and Canadian databases use a default value of one hour to represent a transfer time for switching travel modes. Because the spokes are simply artificial bridges between the facilities and the transport networks, length is an unreliable estimate of the distance a TEU must travel if it is transferred from one mode to another at a facility, so HOURS becomes a constant value, or a facility specific transfer time value if the data are available. DWELL TIME nodes are assigned HOURS values based on published railroad industry values for dwell times at each major station, with industry averages used at minor or unreported stations.

By default, a File Geodatabase creates and updates a SHAPE_LENGTH Attribute for each polyline feature class. With an Equidistant Conic projection, the unit of measure is the meter. KILOMETERS are calculated by dividing SHAPE_LENGTH by 1000, and MILES are calculated by multiplying SHAPE_LENGTH by 0.000621371192.

The values used in the SPEED attribute are derived differently for each polyline feature. While the GIFT cost factor management and calculator allows the user to specify an average speed for a given mode (which is reported in the TIME attribute), SPEED is entered directly into the feature class database. For NTAD and Canadian roads, typical or posted speeds for road class are used, based on published government estimates. Commercial road databases often have “real time” speeds reported, but this project opted to use the publicly available NTAD database for roads and rail network features. Rail speeds are a constant value from the literature, since rail companies have not made available GIS databases with posted or actual speeds. The waterways, derived from the STEEM database from the University of Delaware, have two speeds – near shore and off shore. A 20 km buffer was used to split and assign waterway segments the appropriate speed.

Adding in CFS Freight Totals, Weights, and Destination Estimated TEUs For this type of analysis, the first OD pair import methods works best.

The spread sheet with the port calculations for distributing TEUs to specific destinations is joined to the stops using the Name attribute field, rather than the RouteName attribute. This prevents double counting totals by automatically assigning the origin port default values of zero.

Solve for the Routes. Run the Network Analyst Traversal Result to ArcMap Script (see details in the next section) Open the Edges attribute table Join the Edges attribute table (RouteID) with the Routes Attribute Table (ObjectID) Export edges Create a unique ID field (DISSOLVEID) for use in a dissolve application (see section Creating

Unique IDs for the Edge Features)

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Add Network Analyst Traversal Result To ArcMap Since the model generates multiple routes over the network from a single port, it can be difficult to identify where and how often routes overlap. Including additional origin ports, such as Oakland and Seattle, further complicates this assessment, since routes originating from those ports will also use some network segments from the initial port (LALB). Overlapping routes are a possible indicator of transport volume, congestion, and usage in freight movement. The following script is provided to allow the user to convert individual routes into segments (edges). The edges can then be combined through the DISSOLVE command (ArcToolbox – Data Management Tools – Generalization – Dissolve), counting segments by unique ID to determine how often a given segment of a network is used in the routing analysis. Knowing freight flow to each destination, multiplied by the number of times a given route segment is used when moving TEUs from a port to a destination, allows the user to estimate truck counts and possibly congestion. It also allows the user to accumulate pollutants for a given segment used in multiple routes.

Script AddNATraversalResultToArcMap.txt (provided by Jay Sandhu, ESRI)

Public Sub AddNATraversalResultToArcMap()

Dim pMxDoc As IMxDocument

Dim pNetworkAnalystExtension As INetworkAnalystExtension

Dim pNALayer As INALayer

Dim pFLayer As IFeatureLayer

Dim pTraversalResultQuery As INATraversalResultQuery

Dim pNATraversalResultEdit As INATraversalResultEdit

Set pMxDoc = ThisDocument

Set pNetworkAnalystExtension = Application.FindExtensionByName("Network Analyst")

Set pNALayer = pNetworkAnalystExtension.NAWindow.ActiveAnalysis

Set pTraversalResultQuery = pNALayer.Context.Result

Set pNATraversalResultEdit = pTraversalResultQuery

'Infer Geometry

pNATraversalResultEdit.InferGeometry "", Nothing, New CancelTracker

'Get the Edges and add as a layer

Set pFLayer = New FeatureLayer

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Set pFLayer.FeatureClass = pTraversalResultQuery.FeatureClass(esriNETEdge)

pFLayer.Name = pFLayer.FeatureClass.AliasName

pMxDoc.FocusMap.AddLayer pFLayer

'Get the Junctions and add as a layer

Set pFLayer = New FeatureLayer

Set pFLayer.FeatureClass = pTraversalResultQuery.FeatureClass(esriNETJunction)

pFLayer.Name = pFLayer.FeatureClass.AliasName

pMxDoc.FocusMap.AddLayer pFLayer

End Sub

To use this script in a network analysis, it needs to be loaded into the map document BEFORE the routes are solved

Click Tools – Macros – Visual Basic Editor

In Visual Basic Editor, click File – Import File

Navigate to the directory with the script, change file type to allow for All Files and select AddNATraversalResultToArcMap.txt (note - macro will load but may not display in Visual Basic Editor)

Click File – Close and Return to ArcMap

For a previously solved route, simply resolve to load the current OD route analysis into memory

Run the macro on the active route set –

Click Tools-Macros-Macros

Highlight the macro AddNATraversalResultToArcMap

Click Run

The macro will not show a status bar as it runs, but will generate two memory feature class layers when complete, (junctions and edges). Edges are the routes broken down into simple two-point segment sets (based on junctions), with new unique IDs for each segment, but retaining the route unique ID as SOURCEOID and the unique feature layer ID as SOURCEID. These edges are not permanent features and will be erased once you close the map document, even the map document is saved. To create permanent features, either in the feature database or as a new shapefile, you will need to export the data.

Right click on the layer you want to export (e.g., Edges)

Select Data – Export Data, navigate to the folder of feature dataset you want to use, and save the file

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Creating Unique IDs for the Edge Features Because the network dataset is comprised of different transportation networks, there is an issue involving the unique IDs generated by the edge extraction of the original route features. Canadian roads and US roads, for example, each have Object IDs ranging from 1 to n, but are separate features classes. Therefore, if the dissolve process is used on SOURCEOID attribute in the edges feature class to generate counts, it is possible to include a count of Canadian road segments with US road segments if both sets have edges with SOURCEOID values of 5, for example. To generate a truly unique ID to edges within a given route, a new, unique attribute needs to be created for the dissolve analysis that combines SOURCEID with SOURCEOID.

Right click on Edges and Open the Attribute Table

On the table, click Options – Add Field o DISSOLVEID o Long Integer o 12

Right click on DISSOLVEID and click Field Calculator o [SourceID] * 1000000 + [SourceOID] o OK

DISSOLVEID is now unique and allows the user to know what network feature class a given edge is from (the first one or two digits) and the unique ObjectID of the original network feature (the last six digits).

Calculating the Number of Times a Network Segment is Used for a Given Port Analysis To create a count statistic for the number of times a given network segment is used in a multiple route analysis from a port and the number of TEUs that move over a given route segment, run dissolve, making sure the output name describes the port and impedance attribute used in the network analysis.

ArcToolbox – Data Management Tools – Generalization – Dissolve o LALB_EDGES_HOURS (the input features) o LALB_EDGES_HOURS_DISSOLVE (the output features) o DISSOLVEID (the dissolve field) o From the Statistics Field dropdown menu, choose DISSOLVEID o Select COUNT as the Statistics Type o From the Statistics Field dropdown menu, choose LALB_DTEUs o Select SUM as the Statistics Type o Add in other attributes and statistical summaries as desired o Uncheck the box for multipart features o Click OK

Use the resulting shapefile to create thematic maps that illustrate route counts and TEU totals by network segment. The following figures illustrate sample analysis results.

Figure 233. LA-Long BBeach total rroute counts

119

per networkk segment (eddge)

Figure 24

4. LA-Long BBeach total T

TEUs per ne

120

twork segmeent (edge)

121

APPENDIX C: Recommended Emissions, Cost and Energy Data Sources

Table 10. Recommended emissions, cost, and energy data sources

Mode Parameter California Specific Data National / International Default Data

On road Emissions EMFAC emission factors by California county and segment vehicle speed (grams of pollutant/mile)

No additional data are required

Cost California Energy Commission highway fuel cost assumption ($/gallon)


Energy "Burden" mode for EMFAC provides fuel consumption (gallons) which can be applied to VMT to calculate miles/gal


Rail Emissions Obtain Segment level load factor (notch setting) data from California Department of Transportation (% of max) - currently trying to track down these data

National default NOx, PM, HC, SOx and CO2 emission factors from recent (April 2009) EPA regulatory background documents. These criteria factors can be speciated into HAP components using NEI data.(g of pollutants/hp-hr or gal).

Cost California Energy Commission railway fuel cost assumption ($/gal)

AEO data can be used as a quality check to insure that the IMO and CEC data are reasonable, also national fuel price data provided in the R-1 submittals made by individual rail companies that operate in the state can also be used as a check on the railway

Energy California specific locomotive fuel consumption data are not readily available

The EPA regulatory background documents noted above include fuel factors for line-haul, short-haul and yard locomotives that can be used (gallons of fuel/hp-hr). Typical line-haul and yard HP ratings can be obtained from other EPA documents to derive fu

Marine vessel

Emissions Identify containerships that frequent California ports using army Corps of Engineers Entrance and Clearance data in conjunction with Lloyds data to quantify vessel characteristics (kW)

Apply California vessel characteristics data to IMO GHG factors and SEPA criteria and HAP factors (g of pollutant/kW-hr) - These baseline emission factors will be updated with more recent and more specific factors developed by ARB, CE-CERT, and IVL.

Cost IMO (inbound vessel) Marine vessel fuel cost adjusted for California bunkered marine fuel costs as reported by the California Energy Commission (outbound) ( $/tons of fuel)

AEO data and fuel price data compiled in support of recent EPA marine vessel regulations can be used as a quality check to insure that the IMO and CEC data are reasonable.

Energy Identify containerships that frequent California ports using Army Corps of Engineers Entrance and Clearance data in conjunction with Lloyds data to quantify vessel characteristics (kW)

Apply California vessel characteristics data to SEPA fuel factors (g of fuel/kW-hr)

Facility Port

Emissions Emission factor data included in the LA/Long Beach emission inventories.

Other port inventories and studies of port-based non-road mobile and stationary equipment.

Cost California Energy Commission marine vessel and highway fuel cost assumptions

Energy Energy use data included in the LA/Long Beach emission inventories or derived from CO2 factors.

Other port inventories and studies of port-based non-road mobile and stationary equipment.

Facility Rail Yard

Emissions Emission factor data included in the Yard emission inventories developed for ARB by the Rail companies that operate in the state

Other rail yard inventories and studies of rail terminal non-road mobile and stationary equipment.

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Mode Parameter California Specific Data National / International Default Data

Cost California Energy Commission railway and highway fuel cost assumptions

Energy Energy use data included in the Yard emission inventories developed for ARB by the Rail companies that operate in the state or derived from CO2 factors.

Other rail yard inventories and studies of rail terminal non-road mobile and stationary equipment.

Facility Truck Transfer

Emissions Emission factor data included in the truck-stop emission inventories (sources to be identified). Use EMFAC emission factors for idling, but will need typical idling times from other idle reduction studies.

Other truck yard inventories and studies of truck terminal non-road mobile and stationary equipment. Look at Dray Fleet model developed by TIOGA for Ken Adler at EPA

Cost California Energy Commission highway fuel cost assumption

Energy Energy use data included in the truck-stop emission inventories or derived from CO2 factors (sources to be identified) Use EMFAC fuel consumption factors for idling, but will need typical idling times from other idle reduction studies.

Other truck yard inventories and studies of truck terminal non-road mobile and stationary equipment.

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Notes:

The status of “Not on rail spoke” is a judgment call where the distance between two terminal facilities is less than 2 km

The status of “Way off” is a judgment call where the distance between two terminal facilities is more than 2 km.

The status of “Good” is a judgment call where the distance between two terminal facilities is within 50 m.

There are a total of approximately 410 major carriers listed from the US NTAD facilities. The status help give a descriptive analysis between two terminal facilities from two different sources. It is determined that there are thirty-four facilities that are listed as “Fair”, one-hundred fifteen facilities listed as “Good”, fifteen facilities listed as “Marginally Off”, two hundred twenty nine facilities listed as “Missing”, and sixteen facilities listed as “Wrong Carriers”. The status is assigned based on the distance between two terminal facilities.

127

APPENDIX E: Calculating Emissions from First Principles

The emissions obtained through the emissions calculator employed in the model are calculated using equations derived from the basic principles of physics. These principles involve the energy, materials content in fuels, engine efficiency etc. This section describes the equations and the associated theory.

The terms utilized in the equations are described below:

Load factor (ξ): This factor is a numerical measure which describes the effective utilization of the output power of the engine under consideration. It is expressed as a percentage of the full available capacity of the engine. So, a 0.5 value of the load factor for an engine implies that only 50% of the full available capacity of the engine is being utilized.

Horse power (hp): The power outputs of the engines are expressed in this standard unit of power.

Engine Efficiency (η): For combustion engines, it is the relationship between the total energy contained in the fuel, and the amount of energy used to perform useful work. It is expressed as a ratio of the energy output to the energy input. The value of 0.35 is commonly used for diesel engines.

Horse power hour (hp-hr)/ Kilowatt hour (KWh): These are derived units of energy. An hp-hr signifies the amount of the work done by an engine rated 1hp in 1 hr. Similarly, a KWh is the amount of work done by an engine rated 1 KW in 1 hr. Although hp-hr is not an SI unit, it is nevertheless utilized in various literatures to express the emission factors or emission intensities. Emission intensities are average emission rate of a given pollutant from a given source relative to the intensity of a specific activity; for example grams of carbon dioxide released per hp-hr energy produced.

The basic theory in calculating the emissions can be summed up by the following equation:

Emissionpollutant = Activity * Emission Factorpollutant

Thus, the total emissions resulting from an activity can be described by a relation between the intensity of the activity and the polluting factor for the activity. The units for the emissions are expressed in either grams/mi or just grams. The units for the emission factors are mostly expressed in grams/hp-hr (and sometimes in grams/KWh).

Note: The total emissions in the model are also expressed in grams/ TEU-mile or grams/ ton-mile.

CalculationofemissionsofCO2andSO2

The emission calculations for carbon dioxide and sulfur utilize the concept of engine efficiency and materials content in fuels. The following paragraphs describe the procedure in a step-by-step fashion.

CO2 emissions

In order to find the emissions, we first calculate the energy produced by the engine for doing a particular task e.g. moving goods from point A to point B. We then find out the energy required (input energy) in terms of gallons of fuel needed to produce the equivalent amount of work. Finally, the knowledge of the carbon content of the fuel used lets us compute the emissions produced by the burning of the requisite amount of fuel for the aforementioned task.

The following equation outputs the energy produced in terms of the amount of work done for a particular task:

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ξ * hpout * (d/v)

where

ξ= load factor for the engine utilized hpout= horsepower (output) rating of the engine utilized d=distance traveled in doing the task (in miles) v=velocity of the equipment used (truck, rail or ship) (in miles per hr) The resultant unit for the above equation is hp-hr. (Note: 1 hp-hr = 0.746 KWh) The above equation gives the work done in terms of the output horsepower. This is different from the input horsepower which is the output horsepower divided by the engine efficiency. Thus, we get the input horsepower or the input energy by utilizing the following equation:

hpin = hpout / η

where η= engine efficiency

Once we have the input horsepower, we can convert it to equivalent units of BTUs (British Thermal Units –> a unit of energy) through the following conversion:

1 hp-hr = 2544 BTUs

On obtaining the total amount of BTUs needed to perform the task, we then calculate the amount of fuel needed in gallons by using the following equation:

Gallons of fuel = BTUin / energy density of fuel

Note: The energy density of fuel is expressed in terms of BTUs/gallon.

The amount of carbon present in the fuel is given by the mass density (expressed in grams/gallon). Thus, we get the total amount of carbon burnt (when the fuel is burned) by using the following equation:

Carbon burned (grams)= Amount of fuel used (gallons) * mass density (grams/gallon)

The principles of chemistry state that the molecular weight of carbon is 44 grams/mol of which 27.29% (12 grams/mol) is composed of carbon and the rest oxygen. In other words, the burning of every 12 grams of carbon releases 44 grams of carbon dioxide. So, we utilize a conversion factor of 3.67 (=44/12) to convert the amount of carbon burned into equivalent amount of CO2.

Thus we get the total amount of CO2 generated (in grams) for a particular task, say transporting goods between two geographic locations.

In order to find the amount of CO2 generated in terms of grams/TEU-mile, we divide the total amount of CO2 by the product of the total amount of TEUs per load and the distance traveled. A TEU is a twenty-foot-equivalent standardized cargo container. If we know the net weight of the cargo in a single container in tons, we can calculate the emissions in terms of grams/ton-mile.

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SO2 emissions

The procedure for calculating the sulfur emissions are similar to that for CO2 emission calculations except for a few minor differences.

The amount of sulfur present in the fuel is usually expressed in ppm( parts per million). In order to convert ppm to an equivalent percentage amount, the following conversion is used:

1000ppm= 0.1 % (by weight of fuel)

The molecular weight of SO2 is 64 grams/mol of which 50% (32grams/mol) is sulfur. Thus, the burning of every 32 grams of sulfur produces 64 grams of SO2. So, a conversion factor of 2 (=64/32) is utilized to convert the amount sulfur burnt into equivalent amounts of SO2.

Calculation of emissions of PM10 and NOx

The resultant emissions for PM10 and NOx are estimated in a different manner. The following equation is utilized in calculating the total emissions for either PM10 or NOx:

ξ * hpout * (d/v) * emission factor

where

ξ= load factor for the engine utilized hpout= horsepower (output) rating of the engine utilized d=distance traveled in doing the task (in miles) v=velocity of the equipment used (truck, rail or ship) (in miles per hr) emission factor = average emission rate of a given pollutant from a given source (expressed in grams/hp-hr )

The resultant unit for the above equation is grams (of either PM10 or NOx).

Note: The emission factors for various modes of transportation (rail, truck, ship) and equipments (RTG cranes, yard holsters etc) are sourced from various literature.

For finding the emissions in terms of grams/TEU-mile or grams/ton-mile, we follow the same procedure as used for CO2 and SO2 emissions.

130

APPENDIX F: Creation of Origin-Destination Volume Flow Model

As GIFT requires location of the origin and destination pair (O/D pair) in order to generate optimized routes and the flows of freight in the region, one of the first important steps is to obtain O/D pairs which reflect the flow of freight for the ports of interest. The Task 3: Model Selection and Modification report identifies the origination and destination data sources used in the study, based on the Task 2: Data Compilation.

Origination andDestinationDataSources

The origins and destinations for the routes were sourced from the 2007 Commodity Flow Survey (CFS) data which is published on the U.S. Census Bureau website (http://factfinder.census.gov). CFS provides a comprehensive picture of national freight flows which includes estimated shipping volumes (value, tons, and ton-miles) by commodity and mode of transportation at varying levels of geographic detail (i.e., national, state, select MSAs/ CSAs). The CFS is a shipper-based survey, and captures data on shipments originating from select types of business establishments located in the 50 states and the District of Columbia. The survey is conducted as a partnership between the Bureau of Transportation Statistics and the U.S. Census Bureau, on a five-year cycle as a component of the economic census (Bureau of Transportation Statistics, 2007). The following characteristics of the data make it an attractive source for freight modeling purposes in the model:

Only available source of data that provides about 71% of the value and 69% of the tonnage of freight transported through the highways

Provides estimated shipping volumes (value, tons and ton-miles) by commodity and mode of transportation at varying levels of geographic detail

CFS data are used as the basis for the Federal Highway Administration’s Freight Analysis Framework (FAF), a model that displays by mode the movement of goods over the national transportation network

Figure 29 gives an example of the listing of CFS data for the Los Angeles-Long Beach-Riverside Combined Statistical Area (CSA). The data lists the dollar value and the tonnage of the total freight flow from the origin area to the rest of the U.S., along with figures for individual origin and destination pairs, which include states (as a whole) and select MSAs/ CSAs within those states. There are also entries which represent areas of the state which are not part of the listed MSAs/ CSAs, and are labeled “Remainder of (State)” as such. In some cases, data was suppressed (shown by S in Figure 29), either because of the requirement of avoiding disclosure of confidential data or because of reasons of poor data quality standards. While importing CFS data, these entries were assumed to be ‘0’ for all purposes. The entries for the States (as a whole) were excluded from the final dataset as they represented the totals for the list of MSAs/CSAs regions and the “Remainder of” regions of the states.

Figure 29

The followShallow dunknown modes as

The most levels of gto mode frclassified and Remacombinatiand BudgRemaindeof the CFtotal of 35not define

The CFS pregion locrepresentewest coas

S

L

S

9. CFS data f

wing modes odraft vessel, Dmodes. For threpresentativ

important feageographic defreight flow em

into 3 categoainder of Stateions, can becoet, 2000; Uni

er of State regS regions in t5 states. Eached to have any

provides the fcated in a CSAed freight flowt represented an Jose-San F

Los Angeles-L

eattle-Tacom

for Los Ange

of transportatiDeep draft veshis deliverabl

ve of the freigh

ature of the Cetail. This hemissions. The

ories: State, Ce (areas of staome part of a iv of Iowa). Tgion. Togethethe context ofh of these 35 sy CSAs/MSA

freight volumA/MSA. Thews. In the moby the follow

Francisco-Oak

Long Beach-R

ma-Olympia, W

eles - Long B

ion are coveressel, Pipeline,le, we consideht movement

CFS dataset is elps in creatine levels of geo

Combined Statate outside thenew complem

Thus, a State ier, they make f California. Tstates also has

As, as per CFS

me between anese data were odel evaluatiowing CSAs: kland, CA Co

Riverside, CA

WA Combine

131

each Area (S

ed in the surv, Parcel, U.S. er the total amt.

that the shippg the listing oographic detatistical Area (e CSA/MSA)mentary area,is comprised oup the State a

The CFS data s a “RemaindeS.

n origin regionused to buildn task, we co

ombined Stati

A Combined S

d Statistical A

Source: Com

vey: For-Hire Postal Servic

mount (tons) o

ping volumesof O/D pairs tail in the CFS(CSA), Metro). A number o, defined as a of CSA(s) anas a whole. Fidefines a totaer of State” re

n located in ad a listing of Ooncentrated on

istical Area

Statistical Are

Area

mmodity Flow

Truck, Privace, or courierof goods mov

s can be obtaithat can be ut dataset can b

opolitan Statisof adjacent MCSA (Office

nd/or MSA(s) igure 30 illusal of 73 selecegion. The re

a CSA/MSA aO/D pair region the three ma

ea

w Survey 200

ate truck, Rail, and other anved across all

ined at varyintilized in the mbe broadly stical Area (M

MSAs, in varioe of Managemand the

strates the conct CSAs/MSAemaining state

and a destinatons which ajor ports on t

07)

, Air, nd

ng mode

MSA), ous ment

ncept As in a es are

tion

the

Figure 30

The focusaforementU.S. Togaccounted

PortCo

The otherprovide frcontainer Commerc

0. California

s in this projectioned ports w

gether, the thred for 52 perce

ntainerSt

r important dareight figures freight that o

ce Statistics C

CFS Region

ct is on modewere consideree port region

ent of the total

atistics

ataset was thespecific to co

originated at thCenter (WCSC

ns

eling the contared as they hans of Los Angl container im

e container traontainer traffihe ports. For

C), maintained

132

ainerized freiandle a major geles-Long Bmports in the U

affic for the thic, a separate this, we utilizd by the U.S.

ght traffic genportion of theach, OaklanU.S. for 2008

hree ports of idata source w

zed the data aArmy Corps

nerating frome total contain

nd, and Seattle8 (See Figure

interest. Sincewas needed toavailable from of Engineers

m the ports. Thner traffic in e-Tacoma 31).

e CFS does no account form the Waterbos (USACE).

he the

not the

orne

Figure 31

Table 11 s2009; US and the vogenerateddomestic container the WCSC

Dthlim

Farm

1. Top 25 Co

shows the forArmy Corps

olume of cargd traffic (outbooutbound shitraffic from t

C (US Army C

Domestic Outbhe contiguousmits of the suoreign Inbounrriving by ma

manufacturing

ntainer Port

rmat of the inof Engineers

go (in tons) haound traffic frpment and ththe ports to thCorps of Eng

bound/ Domes and non-conubject port. nd/ Foreign R

arine vessel fog warehouses.

s 2008 (Sour

formation thas). The data inandled at the pfrom the portshe foreign inbohe U.S. mainlagineers, 2008)

estic Shipmenntiguous states

Receipt: Inbouor direct U.S.

133

rce: BTS, U.S

at was obtainencludes contaports. Since t

s to the U.S. mound receiptsand. To facili) are listed be

nt: Traffic movs and territori

und merchandconsumption

S. DoT)

ed from the Wainer traffic (inthe focus wasmainland), wes when calculaitate understanlow:

ving from onies of the U.S

dise originatinn and entries i

WCSC (U.S. Cn TEUs) hand on modelinge took into acating the tota

anding the term

ne location to S.) where the o

ng in foreign into custom b

Census Bureadled at the po

g the port count the

al outbound minology use

another (withorigin is with

countries andbonded storag

au, orts

ed by

hin hin the

d ge and

Table 11.

To get a mcontainers

Another inwas utilizby the totamean tonsin a TEU

Note: Thefind for oudescribed

Obtaini

In creatinginvolved westimate hthe rest ofthe port redistributio

Equation

Where,

an

. West Coast

measure of thes and the tota

nput value thed in the emial outbound ss per TEU wato be 10 tons

e CFS freight ur purpose. Itin the follow

ngtheFre

g the databaswas to calculahow freight gef the U.S. Theegion to the don used of the

n 2

= Origin (por= Destination = Freight fl

= Tons nd = 1, 2, 3 (for = 1, 2, 3, 4,…

t Port Statist

e total port-gel foreign inbo

at was derivessions calcula

shipment in Tas estimated to. This can be

figures and tht is to be emph

wing section c

ight Distri

e of the originate the distribets distributede distribution different regioe following eq

rt region) n (MSA/CSAlow from to of freight flow

the three por…,123 for each

tics (Source:

enerated traffound loaded c

ed from the Uator. It was ca

TEUs for eacho be ~ 11 tone adjusted for

he statistics frhasized that tan be replicat

ibution

n-destination bution of freigd from the oriof freight wa

ons of the U.Squation:

A, Remainder (as percent

w from to

rt regions) h

134

USACE)

fic, we summecontainers.

SACE data walculated by d

h port of interens. For simplic

final case stu

from WCSC wthe model is dted for a comp

pairs that repght tonnage asigins of intereas obtained asS. as defined b

∑∙

of State etc) of total freig(as obtained

ed up the tota

was the averagdividing the toest and then acity we assumudy definition

were the best data-neutral. T

mparable sourc

present contais per the CFSest – the threes a percent of by CFS. The e

100

ght flow from from CFS)

al domestic ou

ge tons per TEotal outboundaveraging acrmed the averan.

available datThe methodolce of data.

iner freight flS figures. Thee west coast pf the total tonnestimation of

to all )

utbound loade

EU. This estimd shipment inross the ports.ge weight of

a that we coulogy that is

ow, the first se rationale waport regions –nage moving f the freight

ed

mate n tons

The cargo

uld

step as to – to out of

As illustraregion of regions astotal of 12of defineddata for thfor this exjust the to

The freightabulated helpful infreight disseen fromitself and originatinSuch an obetween a

Figure 32

ated by the painterest. Thes

s defined in th23 O/D pairs wd CFS regionshose states whxclusion was total of the tonn

ht distributionin a Microsof

n visualizing hstribution obt

m the map the to destination

ng from the Loobservation is an O/D pair is

2. CFS Freig

arameters of Ese 123 destinahe CFS datasewere identifies. The reasonhich were defto eliminate anage figures f

n, as obtainedft Excel file ahow freight mtained for the majority (~87

ns located witos Angeles-Lin accordanc

s inversely rel

ght Distribut

Equation 2, thations includeet, and the Staed from the Cn for this diffefined to have any extraneoufor the MSAs

d through Equalong with the

moves in the gorigin region7%) of the frethin the state ong Beach-R

ce with the gralated to the di

ion for LA/L

135

here were a toed the MSA/Cates for which

CFS dataset. Nerence can bea CSA/MSA

us data as the s/CSAs and R

uation 2 for eae list of the O/geospatial conn of Los Angeeight originatof California

Riverside CSAavity model oistance betwe

LB

otal of 123 deCSA regions ah there were nNote that this e attributed to

and a ‘Rematonnage figur

Remainder reg

ach of the thre/D pairs. The

ntext. For exameles-Long Beating from the a. Less than 15A moves to loof freight traneen the O/D p

estinations forand the Remano defined CSfigure is less the exclusion

ainder of’ regires for the stagions of the st

ee port regione freight distrmple, Figure ach-Riversideregion moves5 % of the totcations outsid

nsport i.e. freipair.

r each origin (ainder of StatSAs/MSAs. Athan the num

n of the state ion. The ratioate level data tate.

ns, was then ribution was 32 shows the

e CSA. As cas within the retal freight de of Californight volume

(port) e

A mber

level onale

were

e an be egion

nia.

136

Accountingforthe“Remainder”regionsoftheStates

In order to provide a better geographical resolution, multiple destinations were represented within the ‘Remainder of’ regions in the 35 states as listed in the CFS dataset. These additional destinations numbered either 2 or 3 for the each of the states and represent other major metropolitan areas within the state not captured by the CSA regions. In splitting the remainder region of a state into multiple destinations, the tonnage figures for the region (as obtained from CFS) were distributed evenly among the derivative destinations. To illustrate the concept, consider the ‘Remainder of Arizona’ region. A total of two destinations were chosen within this region – ‘Remainder of Arizona 1’ and ‘Remainder of Arizona 2’. Thus, we have

Tonnage for Remainder of Arizona = 2,008 tons Tonnage for Remainder of Arizona 1 = 2,008 / 2 = 1,004 tons Tonnage for Remainder of Arizona 2 = 2,008 / 2 = 1,004 tons

For a region with 3 destinations, the tonnage figures were split in 3 parts. This additional number of derivative destinations increased the O/D pair list by 27 to bring the total count of the O/D pairs to 150.

ProcessingthedataforCalifornia

Since the objective of the project is to develop a California-specific intermodal freight transport analysis, the data obtained from CFS were tailored to enable a better resolution for the estimation of the energy and environmental impacts of freight movement through California. In this process, we considered three different approaches. They are discussed in the following paragraphs.

Approach1–DistributingFreightattheCSA/MSALevel In this approach, the list of destinations was the same as specified in the CFS dataset. Thus, California had a total of 5 destination regions – 4 CSAs and the Remainder of the state. The list of the CSAs/MSAs is mentioned below:

San Jose-San Francisco-Oakland, CA Combined Statistical Area

Los Angeles-Long Beach-Riverside, CA Combined Statistical Area

Sacramento--Arden-Arcade--Truckee, CA-NV Combined Statistical Area (CA Part)

San Diego-Carlsbad-San Marcos, CA Metropolitan Statistical Area

The distribution of freight to these destination regions was obtained using Equation 2, as explained in previous sections. This was the default level of resolution of freight distribution for California. The list of the destinations outside the state of California was kept the same as was listed in the original CFS dataset. Thus, the number of total O/D pairs was 150.

Approach2–DistributingFreightattheCountyLevel In this case, the freight destined for the CFS regions within the state of California was disaggregated at the county level. The purpose was to achieve a higher resolution for analyzing freight movement in the state. As in the previous case, the list of the destinations outside the state of California was kept the same as was listed in the original CFS dataset. The process involved finding the list of all the counties (a total of 58) in California, along with their 2007 population estimates. These data were obtained from the California Department of Finance (State of California Department of Finance, 2009). Counties within each of the 5 regions specified in the CFS dataset were identified and categorized accordingly. The county distribution among the CFS regions is highlighted in Figure 33 and is summarized below:

137

San Jose-San Francisco-Oakland, CA Combined Statistical Area 9 counties

Los Angeles-Long Beach-Riverside, CA Combined Statistical Area 5 counties

Sacramento--Arden-Arcade--Truckee, CA-NV Combined Statistical Area (CA Part) 4 counties

San Diego-Carlsbad-San Marcos, CA Metropolitan Statistical Area 1 county

Remainder of CA 39 counties

Having found out the number of counties in a CFS region and their respective populations, the next step was to obtain a population distribution across the counties within each region. The rationale behind this step was the assumption that the population of a region would be a deciding factor in attracting freight to that region i.e. population drives consumption. Obtaining population distribution for the counties would then enable the estimation of the freight distribution at the county level for California. To calculate the population distribution across the counties, the following equation was utilized

∑ ∑ ∙ 100

Equation 3

Where, = Weighted population of county in CFS region (as percent of the total population of

region ) = Population of county in CFS region

and = variable, for each (dependent upon number of counties in a particular region) = 1 to 5 (for the five CFS defined regions in CA)

The weighted population values for each of the counties indicated the distribution of population across the counties in a region. The values are tabulated in Figure 33. For example, it can be seen that Los Angeles County has 56.3% of the total population of all the counties in the Los Angeles-Long Beach-Riverside, CA Combined Statistical Area. This implies that Los Angeles County would have the highest attraction for freight amongst the counties which make up the CFS region.

The final step was to estimate the freight distribution based on the calculated population distribution across the counties in California. In effect, it meant the freight distribution for a region was weighed by the population distribution of the region. The following equation illustrates this concept:

Ϝ

∑ ∑ ∙∑

∙ 100

Equation 4

Where, = Origin (port region) = Destination region (MSA/CSA, Remainder of State) = County within region

= Population of county in CFS region = Tons of freight flow from to (as obtained from CFS)

Ϝ = Freight flow from to (as percent of total freight flow from to in which resides)

and

Figure 33Californi

= 1, 2, 3 (for = 1 to 5 (for t = variable, fo

3. CA Countiia)

the three porthe five CFS

for each (dep

ies and assoc

rt regions) defined regiopendent upon

ciated CFS re

138

ons in CA) n number of co

egions (Sour

ounties in a p

ce: Californi

particular regi

ia Dept of Fi

ion)

inance, State of

This levelregions. Dincreased the freighpopulation

Figure 34

Figure 34made up o(CCD). Asubcountyby the CeCCDs havMCDs doCCDs as t1994). A pincorporaCensus Drules for eare requiror borougcross coun

L

O

R

S

V

Figure 35incorporapopulationapproxim

l of resolutionDisaggregatin

the total numht distribution n of the count

4. Census Ge

illustrates thof subdivisionAn MCD is a y administratinsus Bureau ive no governmo not exist or athe county suplace can be cted under theesignated Plaestablishing ared. Most of thghs. Incorporanty boundarie

Los Angeles C

Orange County

Riverside Coun

an Bernardin

Ventura Count

shows, for exted places win estimate of ately 4 millio

n resulted in tg the 5 CFS r

mber of O/D pat the countyties which ma

eographic Ar

e hierarchicalns- typically clegal entity wive units. A Cin cooperationmental or admare insufficien

ubdivisions, nconsidered aslaws of the s

ace (CDP). CDan incorporatehe incorporatated places does (US Censu

County 88 in

y 34 incorp

nty 24 inco

o County 2

ty 10 incorp

xample, the lathin the coun95 and the la

on (State of C

the inclusion oregions withinpairs to 203. Cy level is the fake up the CS

A

reas (Source:

l relationship called a Mino

with a governmCCD is a statis

n with the Staministrative funt for census ot both. Califs a subdivisiostate or can beDPs, as in theed place. In thted places havo not extend inus Bureau, 199

ncorporated c

porated cities

orporated citie

24 incorporate

porated cities

ayout of the inty numbered argest being Lalifornia Dep

139

of additional n the state of Comparing Eqfreight distribSA/MSA regi

Approach3In this sce

US Census G

between a coor Civil Divisimental unit anstical equivalate officials aunctions. Theystatistics data

fornia has 386n of an MCDe a statistical e case of CCDhe case of Cave strong locanto more than94).

For this prcities

es

ed cities

s

ncorporated a88 with the s

Los Angeles wpartment of Fi

destinations fCalifornia intquation 4 with

bution at the Cion.

–Distributenario (the ne

GARM Ch2)

ounty and an iion (MCD) ornd legal bounent of an MC

and census stay are establisa purposes. So6 CCDs (Bure

D or CCD. A pequivalent in

Ds lack separaalifornia, a mial governmentn one state an

roject, we con

areas in LA Csmallest beingwith an estimainance, 2009)

for the three pto their respeh Equation 2,

CSA/MSA lev

tingFreighext higher lev

)

incorporated r Census Cou

ndaries. MCDCD, which hasatistical areas shed in placeso, a state has eau, 1994; USplace can be e

n which case iate governmeinimum of 50ts and are citi

nd in Californ

nsidered only

County. The tog Vernon withated populatio). Most of the

port origin ctive counties, it can be seevel weighed b

htattheSubvel of resolutio

place. A coununty Division

Ds are thus pris been designcommittees.

s where eithereither MCDs

S Census Bureither legally it is referred tnts. There are

00 registered vies, towns, vil

nia they do no

the incorpora

otal number oh a 2007 on of e incorporated

s en that by the

b‐on)

nty is

imary ated

r s or reau,

o as a e voters llages t

ated

of

d

areas are iunincorpothat are ge

Figure 35

The proceobtaining incorporaFinance wacross thecity of Lodistributio

using Equ

in the vicinityorated areas inenerally remo

5. LA County

ess for obtainithe distributited cities with

website(State e incorporatedos Angeles haons were then

uation 4), as il

y of the city on the county. ote and sparse

y Incorporat

ing the freighion at the couhin the five coof California

d cities was cas about 56%

n applied to th

llustrated by ϝ

of Los AngeleThese areas,

ely populated

ted Places (So

ht distributionnty level. Thounties. ThesDepartment o

alculated, as pof the total po

he freight dist

ϝ

140

es itself. Anotalso referred with ill-defin

ource: LA C

n at the city lehe first step wse data were oof Finance, 2percent of theopulation in Ltribution obtai

∙

∑ ∑

ther point of nto as balance

ned boundarie

ounty Cham

evel was similwas to obtain tobtained from009). Then, t

e population oLA County. Tined at the co

∙∑

∙ 1

note is the exie of county, coes (US Censu

mber of Comm

lar to that follthe population

m the Californthe populatioof the county.These city-levounty level (w

100

istence of ontain territor

us Bureau).

merce)

lowed for n of the identi

nia Departmenon distribution. For example

vel populationwhich was obt

ries

ified nt of n e, the n tained

141

Equation 5.

ϝ

∙

∑ ∑ ∙∑

∙ 100

Equation 5

Where, = Origin (port region) = Destination region (MSA/CSA, Remainder of State) = County within region = Incorporated city within County within region = Population of city within County

= Population of county in CFS region = Tons of freight flow from to (as obtained from CFS)

ϝ = Freight flow from to (as percent of total freight flow from to in which resides) and

= 1, 2, 3 (for the three port regions) = 1 to 5 (for the five CFS defined regions in CA) = variable, for each (dependent upon number of counties in a particular region) = variable, for each (dependent upon number of incorporated cities in a particular county)

Comparisonofthethreefreightdistributionapproaches

The three approaches can be summarized by Figure 36.The three approaches to distributing freight in the state of California were discussed to demonstrate the flexibility of modeling freight distribution at varying levels of geographic detail. Any of these approaches can be utilized for the California-specific GIFT model, provided there is accurate data available on the movement of goods between the port regions and the aforementioned destinations i.e. CSA/MSA regions, counties and incorporated cities.

Figure 36

The three flexibilitycan be utimovemencounties a

Applyin

As mentiomoving inof freight the study explainedfrom the AgeneratedCFS datasthe contaifollowing

Equation

Equation

6. Distributin

approaches ty of modeling ilized for the Cnt of goods beand incorpora

gthefreig

oned before, tn between O/Dmovement (cwas to model

d in the previoArmy Corps o

d container traset. Applyinginer traffic (ing sets of equat

n 6

n 7

ng freight flo

to distributingfreight distri

California-spetween the poated cities.

ghtdistribu

the dataset avD pairs in termcontainerized l containerizeous sections) wof Engineers Waffic would fog the CFS freign TEUs) betwtions:

w

g freight in thebution at varyecific GIFT mrt regions and

ution toth

vailable from ms of TEUs. and bulk) bet

ed freight flowwas applied toWCSC. The p

ollow the samght distributio

ween O/D pair

142

e state of Calying levels ofmodel, providd the aforeme

hePortgen

CFS does notIt lists the tottween origins

w, the freight o the port conpremise behin

me distributionon to the portrs (O/D TEUs

ϝ ∙

Ϝ ∙

lifornia were df geographic dded there is acentioned desti

neratedtra

t list the amoutal tonnage ams and destinatdistribution o

ntainer trafficnd doing so wn pattern as obt generated cos). This estim

discussed to ddetail. Any ofccurate data ainations i.e. C

affic

unt of containmount which tions in the Uobtained fromc figures that wwas the assumbtained for freontainer gave

mation was der

demonstrate tf these approa

available on thCSA/MSA reg

nerized freighincludes all k

U.S. As the focm the CFS datwere obtained

mption that theeight from thean estimation

rived using th

the aches he gions,

ht kinds cus of ta (as d e port e n of he

143

∙

Equation 8

Where, = Origin (port region) = Destination region (MSA/CSA, Remainder of State) = County within region = Incorporated city within County within region ϝ = Freight flow from to (as percent of total freight flow from to in which resides) (from

ϝ

∙

∑ ∑ ∙

∑∙ 100

Equation 5) Ϝ = Freight flow from to (as percent of total freight flow from to in which resides) (from Equation 4)

= Freight flow from to (as percent of total freight flow from to all ) (from Equation 2) = Port generated container traffic in TEUs for port region = Container traffic in TEUs from origin port to incorporated city = Container traffic in TEUs from origin port to county = Container traffic in TEUs from origin port to region

The last three terms listed above represent the estimated O/D TEUs at varying levels of geographic details (as explored by our three different approaches to freight distribution). Figure 37 illustrates the complete process workflow for our three approaches to freight distribution. Thus, our methodology of assigning freight to destinations at varying levels of geographic detail (county and incorporated city) is population-based. This methodology, as mentioned before, stems from the assumption that population drives consumption and hence, is a factor influencing freight attraction for a region.

Figure 37

The next sports was destinatiochosen to intermodafacilities ifor the weThis destiCaliforniaapart fromto be FlagMSA/CSA

7. Process W

step in buildinvisually verins, a similar pbe a centrally

al facility – if in the region. est coast portsination was cha, the locationm the listed Mgstaff and RemAs for the stat

orkflow for f

ng the freightfied on Googprocess was fy located poinf it existed – o

Figure 40 shs. Within the hosen to be thns for the “Re

MSA/CSA for mainder of Arte. Once agai

freight distri

t flow model gle Maps™ anfollowed. In cnt in the largeor an industriahows the overstate of Calif

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144

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APPENDIX G: Cambridge Systematics Inc FAF2 Disaggregation Methodology

We studied another data set which was provided to us by CARB. This data set, developed by Cambridge Systematics Inc., used the Freight Analysis Framework 2.0 (FAF2) data, based on 2002 CFS data, to obtain O/D tonnage figures at the county level for California. The methodology adopted by Cambridge Systematics (CS) was different than the one adopted by the GIFT team for estimating the O/D tonnage figures at the county level for California. The following section briefly explains the CS methodology as a point of comparison.

Cambridge Systematics used regression analysis to generate equations for production and attraction for the counties in California and other FAF2 regions outside of California (Since the FAF2 data was generated from 2002 CFS, the FAF2 zones share their boundaries with the CFS defined CSAs/MSAs and “Remainder of” regions).

For the regression equations, the tonnage figures were the dependent variable. The explanatory variables were factors which thought to affect the amount of a commodity produced in a region or destined for a region, such as employment by industry (using the North American Industry Classification System), total employment, population etc. Thus, a region with zero employment in an industry would not produce/attract any freight in commodities associated with that industry.

The production and attraction equations were generated by commodity groups as shown on the following page.

(Here the explanatory variables includes employment in industry that produce the specific commodity group along with other variables)

(Here the explanatory variables include employment in industry that consume the specific commodity group along with other variables)

Using the above two regression models, the production of a particular commodity in a county- Pc (i); and the attraction of a particular commodity to a county- Ac (i) were estimated. These figures were then aggregated to compute the production (or attraction) of a particular commodity in the FAF2 zone which the counties were associated with. The following equations illustrate the concept

PFAF (i) = ∑

AFAF (i) = ∑

Finally, the ratio of the county production (or attraction) to the FAF2 zone production (or attraction) was utilized to break down the original 114 by 114 FAF2 O/D pair database to the county level ,which resulted in a 3140 by 3140 O/D pair database, thereby including all the counties in the US. The following equation sums this process:

148

, ,

P

A

For California, the figures were adjusted for modal accessibility.

In this way, the Cambridge Systematics FAF2 disaggregated database provided direct figures for the percent of freight flowing from a CSA/MSA (or a port region) to a particular county.

Download - Development of a California geospatial intermodal freight ... · PDF fileDevelopment of a California Geospatial Intermodal ... from road to rail. The Case Study estimates ... three

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